diplomarbeit - E

Transcription

diplomarbeit - E

DIPLOMARBEIT
Embedding the Country-of-Origin
in the Corporate Brand Name:
an Empirical Study
Paul René Frigo
Angestrebter akademischer Grad
Magister der Sozial- und Wirtschaftswissenschaften
(Mag. rer. soc. oec.)
Wien, im Februar 2011
Studienkennzahl lt. Studienblatt:
157
Studienrichtung lt. Studienblatt:
Internationale Betriebswirtschaft
Betreuer:
Univ.-Prof. Dr. Adamantios Diamantopoulos
To life
Am Gelingen dieses Werks hatten viele Personen teils erheblichen Anteil. Zuallererst gilt mein großer
Dank Professor Adamantios Diamantopoulos, meinem Betreuer, der mich in allen Situationen mit
größtem Verständnis unterstützt und gefördert hat. Auch danke ich dem gesamten Chair of International
Marketing, insbesondere Katharina Zeugner-Roth und Birgit Löhndorf. Große Dankbarkeit verspüre
ich auch gegenüber der Deutschen Bank und dem Team „Land der Ideen“, insbesondere gegenüber
Maike Tippmann, die mir dabei geholfen hat, mein wahres Potenzial zu erkennen und mich geduldig
und mit großer Weitsicht unterstützt hat. Danke auch an meine beiden Lektoren Frauke Grunow und
Simon Hiscox. Dank ihnen ergibt diese Arbeit auch sprachlich Sinn.
Meiner Mutter, Erna Fürtner danke ich für ihre grenzenlose Liebe und ihre unbändige Kraft, die auch
mir ebensolche verliehen hat und meinem Vater, Peter Frigo, besonders dafür, dass er mir dabei hilft,
meinen eigenen Weg zu finden und ihn auch zu gehen. Meinen drei Geschwistern Petra Fembek,
Christine Solchinger und Peter Frigo danke ich, dass Sie mich bedingungslos bei allem unterstützen.
Zum Abschluss möchte ich noch drei Personen hervorheben: Johanna Feuerstein, Odo Dvorak und
Michel Tragschitz, das Triumvirat meiner Seele; Michel, der mich immer zu Höchstleistungen antreibt,
Odo der mich wieder auf den Boden der Tatsachen zurückholt und Johanna, die allein durch ihre reine
Anwesenheit Wunder bewirken kann (und bewirkt). Ohne euch wäre ich ein schlechter Mensch.
I Eidesstaatliche Erklärung
Ich erkläre an Eides statt, dass ich die vorliegende Arbeit selbständig und ohne fremde
Hilfe verfasst, andere als die angegebenen Quellen nicht benützt und die den benutzten
Quellen wörtlich oder inhaltlich entnommenen Stellen als solche kenntlich gemacht
habe.
Wien, im Februar 2011
II Abstract
Past research has expressed doubt on the relevance of the country of origin (CoO) of
products, brands and services in evaluating and/or consuming them. The present study
has analysed the influence of the image of the CoO of a brand on the image of the brand
itself and the intention to get in contact with it via a multicountry study. Our results
provide strong support to the ongoing economical importance of CoO. It could be
proved that the more consumers are (made) aware of the (desired) origin of a brand, the
higher the magnitude of above-mentioned influence is. Furthermore, the effect of a
series of variables on the evaluation of countries and brands and intention to consume
the latter has been tested for. It has been found that higher levels of consumer
ethnocentrism do not necessarily lead to derogation in the evaluation of foreign
countries and brands, as well as behavioural intention towards the brand. Furthermore,
sociodemographic characteristics of consumers were found to have no consistent
influence on either of the three. Additionally, results reveal that higher familiarity with a
country or an industry does not necessarily lead to a more positive evaluation of
associated brands. Altogether this piece of work provides important insights on the
functioning of the CoO cue and associated constructs, them being valuable to both,
researchers and marketing practitioners. Academics get valuable insights into the
functioning of CoO effects and find further proof for its’ relevance. The latter can use
the results in developing the communication strategy for their brand(s).
III Content
List of Figures................................................................................................................. VI
List of Tables .................................................................................................................VII
List of Appendices ....................................................................................................... VIII
1. Introduction................................................................................................................ 1
1.1 Research Objective ............................................................................................... 2
1.2 Structure ............................................................................................................... 3
2. Literature Review....................................................................................................... 4
2.1 Country of Origin ................................................................................................. 4
2.1.1 Relevance of CoO........................................................................................... 5
2.1.2 Country of Origin Image................................................................................ 9
2.1.3 Measurement methods of CoI ...................................................................... 11
2.1.4 Country of Origin Effects ............................................................................. 13
2.1.5 Service industry............................................................................................ 16
2.1.6 Conclusion ................................................................................................... 17
2.2 CoO and the Brand ............................................................................................. 18
2.2.1 Brand Image as a Summary Construct ........................................................ 19
2.2.2 Brand Origin Recognition............................................................................ 21
2.3 CoO and the Consumer ...................................................................................... 23
2.3.1 Consumer Animosity .................................................................................... 25
2.3.2 Consumer Ethnocentrism ............................................................................. 27
2.4 CoO and Familiarity ........................................................................................... 29
2.4.1 Familiarity as a Summary Construct ........................................................... 31
3. Research Question, Hypotheses & Model ............................................................... 32
3.1 Research Question .............................................................................................. 32
3.2 Hypotheses & Model .......................................................................................... 33
IV 4. Methodology ............................................................................................................ 38
4.1 Study Design ...................................................................................................... 38
4.2 Questionnaire Development ............................................................................... 39
4.3 Data analysis....................................................................................................... 42
4.4 Sample ................................................................................................................ 43
5. Results...................................................................................................................... 46
5.1 Preliminary Analysis .......................................................................................... 46
5.1.1 Data Screening and Descriptive Statistics................................................... 46
5.1.2 Cross-Tabs ................................................................................................... 49
5.1.3 Comparison of Means .................................................................................. 51
5.1.4 Correlation Analysis .................................................................................... 52
5.2 Main Analysis..................................................................................................... 54
5.2.1 Regression analysis on BeI .......................................................................... 54
5.2.2 Regression analysis on BeI .......................................................................... 58
5.3 Further Analysis ................................................................................................. 61
6. Discussion ................................................................................................................ 64
6.1 Composition of Brand Image ............................................................................. 65
6.2 Composition of Behavioural Intention of Brands .............................................. 69
6.3 Composition of Country Image .......................................................................... 70
7. Conclusion ............................................................................................................... 72
7.1 Managerial Implications ..................................................................................... 74
7.2 Limitations and Further research........................................................................ 75
8. List of important abbreviations ................................................................................ 77
9. List of references...................................................................................................... 78
10. Appendices ............................................................................................................. 90
V List of Figures
Figure 1: Hypothetical Model for BI and BeI................................................................. 36 Figure 2: Hypothetical Model for CoI ............................................................................ 37 Figure 3: Semantic Differential Scale for Images of Stimuli (Spain)............................. 47 Figure 4: Semantic Differential Scale for Images of Stimuli (Italy) .............................. 47 VI List of Tables
Table 1: Two Dimensional Consumer Segmentation Model.......................................... 25 Table 2: Study design ..................................................................................................... 38 Table 3: Sample characteristics by country .................................................................... 44 Table 4: Cronbach’s Alphas for stimuli.......................................................................... 48 Table 5: Descriptive Statistics of Stimuli (Spain) .......................................................... 48 Table 6: Descriptive Statistics of Stimuli (Italy) ............................................................ 48 Table 7: BORA-rates (Spain) ......................................................................................... 50 Table 8: BORA-rates (Italy) ........................................................................................... 50 Table 9: Regression Analysis on BI (I) .......................................................................... 55 Table 10: Regression Analysis on BI (II) ....................................................................... 56 Table 11: Regression Analysis on BeI (I)....................................................................... 59 Table 12: Regression Analysis on BeI (II) ..................................................................... 59 Table 13: Regression analysis on CoI (I)........................................................................ 62 Table 14: Regression Analysis on CoI (II) ..................................................................... 63 VII List of Appendices
Appendix A: Questionnaire (English) ............................................................................ 90 Appendix B: Questionnaire (Spanish) ............................................................................ 96 Appendix C: Questionnaire (Italian)............................................................................. 102 Appendix D: Descriptive Statistics of Familiarities ..................................................... 108 Appendix E: Descriptive Statistics of Behavioural Intention....................................... 108 Appendix F: Independent Samples t-test for Intercountry Comparison of Stimuli...... 109 Appendix G: Paired Samples t-test for Intracountry Comparison of BI....................... 110 Appendix H: Paired Samples t-test for Intracountry Comparison of Familiarities ...... 111 Appendix I: Independent Samples t-test for Intercountry Comparison of BeI............. 112 Appendix J: Paired Samples t-test for Intracountry Comparison of BeI ...................... 112 Appendix K: Correlation of Stimuli and Familiarities (Spain)..................................... 113 Appendix L: Correlation of Stimuli and Familiarities (Italy)....................................... 119 Appendix M: Multiple Regression on BI (DB, Spain) ................................................. 125 Appendix N: Multiple Regression on BI (CB, Spain) .................................................. 126 Appendix O: Multiple Regression on BI (DB, Italy).................................................... 127 Appendix P: Multiple Regression on BI (CB, Italy) .................................................... 128 Appendix Q: Multiple Regression on BeI (DB, Spain) ................................................ 129 Appendix R: Multiple Regression on BeI (CB, Spain) ................................................ 130 Appendix S: Multiple Regression on BeI (DB, Italy) .................................................. 131 Appendix T: Multiple Regression on BeI (CB, Italy) .................................................. 132 Appendix U: Multiple Regression on CoI (Spain) ....................................................... 133 Appendix V: Multiple Regression on CoI (Italy) ......................................................... 134 VIII Appendix W: Abstract (German).................................................................................. 135 Appendix X: Curriculum Vitae..................................................................................... 136 IX Introduction
1. Introduction
Häagen-Dazs is Danish, Coca Cola American and Red Bull also emerges from the US.
Two out of these three statements are wrong. Which ones, due to the fact that Coke may
be characterised as the most American of all American brands, seems to be an easy
guess. However, when not in possession of this knowledge, all three origin definitions
may be judged as correct, due to various reasons. Häagen-Dazs is actually American
and chose this brand name to sound European and therefore be associated with the
according image. Red Bull is Austrian but clearly refrains from using any origin cue
whatsoever. Austrians know where it comes from and (most of) the rest of the world,
due to its’ trendy and hyper-modern image, think it to be American.
“Origin information is provided to consumers through hundreds of thousands of brand
and company names, promotional messages, product labels, and other means, whether
directly or through symbolism. In short, the images of countries and their relationships
with products are an integral part of daily life” (Papadopoulos, 1993, p. 16). The usage
of origin information, however, is not just a recent development. It has “played a
significant role throughout history in enabling people to identify, classify, assess, think
of, and act upon phenomena and objects” (p. 9). Examples therefore are definitions like
Greek mythology, Russian roulette, London Fog, British rock, or Mexican standoff.
Furthermore, associations like German engineering, Japanese technology, Danish and
Swiss chocolate or Afghan rugs, further accentuate the usage of origin references for
products (Papadopoulos & Heslop, 2002).
In this context it is essential for companies and organisations, as well as academic
researchers, to know how and to what extent the origin cue influences the evaluation of
brands, product and services as well as the decision to buy them. This is of utmost
importance, especially in situations where the country of origin (CoO) is constantly
being communicated by the company or organisation.
One such example is companies bearing their CoO in their corporate brand name (e.g.:
American Apparel, Gaz de France, Russian Standard Vodka, Holland Blumen Mark,
Deutsche Bank). These companies have no choice but to constantly communicate their
CoO, as the following quote from an article in the German magazine “DER SPIEGEL”
1 Introduction
about Deutsche Bank illustrates: “Its’ position as ‘Germanys’ biggest and most
successful financial institute […] is by no means a simple one. Already the ‘Deutsche
[Editors’ Note: German]’ in its’ name leads to a ‘rather distant relationship’ among
many people” (Kazim, 2008). So, even though a brand may possess of a very strong
image itself, its’ CoO may still bear significant (positive or negative) influence. It seems
only reasonable to conclude that the influence CoO has on a brand is higher for
companies having their origin embedded directly in their corporate brand name (and are
thus constantly communicating it), than for companies, where this is not the case.
What is suggested above, however, lacks scientific confirmation. Even though CoO and
its’ implications are “arguably the most researched field in international marketing”
(Heslop et al., 2008, p. 356), the impact on companies presumably being most
confronted with the so called country of origin effects (CoE)1 (i.e., those having their
CoO embedded in their corporate brand name) has not yet seen attention in scientific
literature.
Furthermore, existing CoO literature has been rather product-oriented regardless of the
increasing importance of the service industry in global economy (Harrison-Walker,
1995; Pecotich et al., 1996; Ahmed et al., 2002; Jaffe & Nebenzahl, 2006; d'Astous et
al., 2008), this representing another gap in academic research.
1.1 Research Objective
This thesis seeks to strengthen knowledge of CoE in the service industry and close
aforementioned literature gaps. It will be analysed whether the influence of CoO on a
brand differs when the origin cue is embedded in the corporate brand name, compared
to when this is not the case.
This work is based upon a multinational quantitative study comparing the influence of
CoO and several other constructs, past research has proven to carry substantial influence
on brand image (brand origin recognition accuracy, the image of the industry, different
types of familiarity, sociodemographic characteristics and consumer ethnocentrism).
1
Defined as „The effect an image of a country has on brands or products related with the country“ (Jaffe
& Nebenzahl, 2006, p. 31)
2 Introduction
The results of this work are beneficial to both, academics and practitioners. Researchers
can extract knowledge about CoE when CoO is visibly and prominently integrated in
company essentials. Furthermore the knowledge on CoO in the service industry is
deepened. Marketing practitioners, on the other hand – especially those of companies
having the CoO embedded in the corporate brand name – can use the results of this
study when deciding on their overall communication strategy.
1.2 Structure
This thesis is divided into 7 parts. Chapter 1 has given you a principal introduction on
the basics and aims of this piece of work as well as the reason of it being of importance
to marketing researchers and practitioners.
Chapter 2 provides an overview on the theoretical background for this thesis. Starting
with an introductory part on history and importance of CoO, it will then cover the
essentials on the other constructs relevant in this piece of work.
Chapter 3 covers the research questions this thesis is addressing as well as the
hypotheses the author has developed.
Chapter 4 covers the technical, i.e., methodological framework including research
method, sample and questionnaire. The chapter concludes with an overview on how the
data from aforementioned questionnaires has been analysed as well as possible
limitations of these procedures.
After having provided the reader with the necessary technical background, Chapter 5
deals with the results having emerged from the empirical study.
In the following part – Chapter 6 – these results are being discussed and the according
implications on academic research as well as marketing practitioners shown.
Chapter 7 provides a short summary of the findings of this thesis. Furthermore,
limitations of this work are covered as well as avenues for further research shown.
3 Literature Review
2. Literature Review
The aim of this chapter is to provide the essential theoretical background on the
constructs used and analysed in this thesis. First, the concept of country of origin (CoO)
is presented, putting a special focus on its relevance and effect. Second, the interaction
between CoO and brand is discussed. The third section covers the implications of
consumer sociodemographics and attitudes. In the last part, the influence of the various
types of familiarity is discussed.
2.1 Country of Origin
The French are well renowned for their expertise in wine and food. Italy and France are
leaders in fashion. The finest cigars are from Cuba. Japan excels in technology, as do
the USA. And if a watch originates from Switzerland, it automatically is judged to be of
good quality.
These common beliefs show that the origin of a product bears important information
about the very product (e.g.: Papadopoulos & Heslop, 1993; Verlegh & Steenkamp,
1999; Ahmed et al., 2002; Roth & Diamantopoulos, 2008), probably working “similar
to a brand name” (Ittersum et al., 2003, p. 223). But what is the CoO? It is usually
defined as the country “which consumers typically associate with a product or brand,
irrespective of where it is actually manufactured” (Usunier, 2006, p. 62). In other
words: It is the country that comes to ones’ mind, when being confronted with a certain
product, service, brand or company. The actual origin(s) therefore bear(s) only little
relevance (c.f.: 1. Introduction). Many researchers have claimed that the term country of
origin is both, too broad and too narrow at the same time, with it being necessary to also
include bigger entities (e.g.: European Union) or smaller ones (e.g.: cities, regions)
which may even span over political borders (e.g.: Roth, 1995; Ittersum et al., 2003;
Jaffe & Nebenzahl, 2006). Therefore, when the term country is being used in this thesis,
it also incorporates geographic entities of different dimensions.
CoO tags bear a huge amount of information, enabling us to “identify, classify, assess,
think of, and act upon phenomena and objects” (Papadopoulos, 1993, p. 9). It is “to a
product what ‘occupation’ is to a new acquaintance we make at a party: we sort of have
4 Literature Review
to ask about it [...] to put our new friend in context” (Papadopoulos & Heslop, 1993, p.
xxii). In other words, CoO provides us with some sort of an anchor in a highly
globalised world (Roth & Romeo, 1992). Consumers use the CoO cue to “to form
preferences and purchase decisions, but it also elicits emotions, feelings, imagery, and
fantasies” (Verlegh & Steenkamp, 1999, p. 522).
Following Papadopoulos (1993), the CoO of a product and/or brand can – apart from
the physical made-in label – be communicated via several methods (c.f.: HarrisonWalker, 1995; Brodowsky et al., 2004). First the CoO may be an integral part of the
company’s (corporate) brand name as is the case with, for example, American Airlines
or Deutsche Bank (c.f.: Pecotich et al., 1996; 2.2.1 Brand Image as a Summary
Construct). Second, the company and/or brand may already be strongly associated with
a certain country, such as Coca Cola (American) or Sony (Japan). Third, a specific
language may be used in worldwide corporate communication. Giorgio Armani evokes
(correct) associations to Italy. Häagen-Dazs, as aforementioned, uses this strategy to
(wrongly) be judged a Danish/Scandinavian brand. This effect can be accentuated by
using the language in the corporate claim, a method that in recent years has been used
e.g., by car manufacturers. Audi advertises with Vorsprung durch Technik
(Advancement through technology), Volkswagen uses Das Auto (THE car) and Renault
communicated itself as créateur d’automobiles (creator of cars). Fourth, companies may
use country-symbols in order to strengthen country associations. One of the best
examples therefore is IKEA, it not only using an animal strongly associated with its’
home country Sweden (the elk) but solely using the colours of the Swedish flag (blue,
yellow) in their logo, store design, communication material, etc. (Papadopoulos, 1993).
2.1.1
Relevance of CoO
Corresponding to above-mentioned examples, past research strongly suggests that CoO
has a significant effect on product evaluation (e.g.: Han, 1989; Roth & Romeo, 1992;
Papadopoulos, 1993; Peterson & Jolibert, 1995; Liu & Johnson, 2005).
In the beginning days of CoO-research, the focus has merely been on proving that
country of origin effects (CoE) exist (Bilkey & Nes, 1982; Papadopoulos, 1993;
Peterson & Jolibert, 1995; Usunier, 2004). Since then the field has evolved to, as
already mentioned, “the most researched field in international marketing” (Heslop et al.,
5 Literature Review
2008, p. 356), with “over 1,200 published works” (p. 356) on this topic (see literature
reviews by Bilkey & Nes, 1982; Papadopoulos, 1993; Baughn & Yaprak, 1993,
Al-Sulaiti & Baker, 1998; Verlegh & Steenkamp, 1999; Roth & Diamantopoulos,
2006).
The ever-recurring question is as to whether CoO is – consciously or subconsciously –
noticed and used by third parties. “Nearly every country of origin study has assumed
that consumers look for the made-in label when judging the characteristics of a product”
(Jaffe & Nebenzahl, 2006, p. 80, c.f.: d’Astous & Ahmed, 1999). This assumption is
criticised by Jean-Claude Usunier (2006), stating that the relevance of CoO has become
“factual common knowledge” (p. 63), even though its’ real world relevance is
decreasing (c.f.: Samiee et al., 2005). For Usunier (2006) several issues have to be
addressed in order to “assess possible discrepancies between COO research and the
‘real world’” (p. 63). Following his line of argumentation it is to be questioned, whether
(1) information on CoO is available at all (2) consumers even use it and (3) CoE
withstands the rise of importance of multinational brands.
“For customs officers, it is now sufficient to have the origin mentioned in customs
documents rather than on the merchandise itself. Consequently, consumers are less
informed about the origin of products, especially when it is unfavourable” (Usunier,
2006, p. 63; c.f.: Samiee, 2010). In fact, at the beginning of the 21st century, a proposal
of Confindustria, an Italian employers’ federation, on mandatory origin declaration for
products imported into the European Union and the introduction of a Made in the EU
Label failed due to opposition of Germany, the UK and the Netherlands (Samiee, 2010).
However, in some of the most economically important countries (USA, Canada and
China) the majority of imported products require origin labelling (Confindustria, 2005).
Furthermore the focus on origin declaration on merchandise reduces CoO to the
identification tag on the inside of a t-shirt. As already mentioned, the origin cue works
via different channels, the mere presence on the product being only one of them. Of
course “the key question is whether the producer elects to emphasize a particular cue
beyond the point necessary by legal requirements” (Papadopoulos, 1993, p. 14).
However, it is argued that marketers often use the origin cue for differentiation of their
products due to the possibility of worldwide production and standardization
(Papadopoulos & Heslop, 2002). Niss (1996), for example, studied the usage of origin
information in Danish companies, interviewing decision makers in top or middle
6 Literature Review
management in a variety of industries. His results indicate that origin cues are used (1)
mostly where the image of the CoO is “considered suitable for the specific type of
product on offer” (p. 14), i.e., when it matches with desired product positioning and (2)
more frequently in the beginning stage of the product life cycle, gradually turning to
brand-name marketing in later stages.
When looking at the proposed decrease in real world relevance, numerous examples
prove CoO to still bear significant importance (Ittersum et al., 2003). For example,
Ettenson and Klein (2005) found French nuclear testing in the South Pacific to have had
significant influence on the intention of Australians to purchase French products,
concluding “a firm may find itself mired in an unforeseen marketing crisis stemming
from a controversial event external to the firm and its marketing activities” (p. 200).
Other examples, such as the call-back of over 4.5 million cars by Toyota in 2010, show
that actions of one single company can affect the image of a whole country (Austin,
2010; c.f.: Jaffe & Nebenzahl, 2006). Furthermore country image campaigns like
Germany – Land of Ideas, 100% Pure New Zealand or South Africa – Alive with
Possibility show that all over the world countries engage in shaping and reshaping their
image in order to foster tourism, investment and exports (Papadopoulos, 1993; c.f.:
Jaffe & Nebenzahl, 2006).
Now, do “consumers still attach importance to the country where a product is
manufactured?” (Usunier, 2006, p. 63). The author bases his assumption on the majority
of people neither knowing nor caring about the country of manufacture (CoM) of
products on several studies prima facie supporting his point of view. However, when
examining more closely the products in question, one finds that with e.g. apparel and
household appliances, his assumption is derived from low-involvement2 goods only.
Ahmed et al. (2004), analysed the impact of CoO, brand and price on the evaluation of
food products, concluding that CoO “does play a role in consumers’ evaluation of lowinvolvement product but its effect is weak” (p. 112). However, even though, according
to Josiassen et al. (2008), consumers place even less importance on the CoO cue, when
evaluating high-involvement products, as opposed to their low-involvement
counterparts (c.f.: Schaefer, 1997), this view is being contradicted by the majority of
researchers. Ahmed et al. (2002), analysed CoO effects in the services industry and
2
involvement defined as: “A person’s perceived relevance of the object based on inherent needs, values,
and interests” (Zaichkowsky, 1985, p. 342).
7 Literature Review
stated, that in situations which “constitute a higher risk for the consumer” (p. 295) the
CoO cue may be of higher importance than in low risk (i.e. low-involvement) situations
(c.f.: Zaichowsky, 1985; Papadopoulos, 1993; Ittersum et al., 2003; Lin & Chen, 2006;
Michaelis et al., 2008; Zeugner-Roth & Diamantopoulos, 2010). Furthermore, the aforementioned study of Confindustria in Italy, France, Germany and the UK, revealed that
consumers are indeed interested in the origin of products, saying that it provides more
information (agreed by approximately 80% of respondents) and helps detecting
products emerging from countries engaging in e.g. child labour (agreed by 70-80%)
(Confindustria, 2005). Brodowsky et al. (2004), state that “[i]n an atmosphere of
renewed patriotism […] Americans are once again talking about where products are
made” (p. 729 f.) and find CoO to be a “moving but not irrelevant target” (p. 730).
Cordell (1992) found 69.2% of US-respondents to seek CoO information at least for
some purchases and Papadopoulos & Heslop (2002) stated consumers to use origin cues
“to ’chunk’ information, reduce perceived risk and assess the social acceptability of
their purchases” (p. 296).
Another criticism of the validity of CoO is rooted in globalization (Usunier, 2006). This
development has brought forward a vast number of products whose actual origin cannot
be clearly defined, so-called hybrid products (e.g.: Häubl, 1996; Al-Sulaiti & Baker,
1998; Ahmed et al., 2002). It is argued that, due to the influence of CoM, country of
brand (CoB) or country of design (CoD) (c.f.: Papadopoulos & Heslop, 2002; Jaffe &
Nebenzahl, 2006), the relevance of CoO is constantly declining (Usunier, 2006).
However, following the definition of CoO as the country “which consumers typically
associate with a product or brand, irrespective of where it is actually manufactured”
(Usunier, 2006, p. 62, c.f.: Ahmed et al., 2004; Jaffe & Nebenzahl, 2006), globalization
provides companies with a wide choice of possible countries of origin (e.g.:
Papadopoulos, 1993; Klein et al., 1998; Verlegh & Steenkamp, 1999; Brodowsky et al.,
2004; Jaffe & Nebenzahl, 2006). The American brand Apple, for example, is, regardless
of where their products are being manufactured, branding them as designed by Apple in
California. This development, the focus on CoB or CoD rather than on the actual
product origin has become a widespread phenomenon for multinational companies (e.g.:
Verlegh & Steenkamp, 1999; Usunier, 2006; Usunier & Cestre, 2007; Koubaa, 2008;
cf.: 2.2.1 Brand Image as a Summary Construct).
8 Literature Review
However, even though “[t]here is enough evidence to confirm that origin does matter
[…], people do not like to admit that it does” (Heslop & Papadopoulos, 1993, p. 68 f.;
c.f.: d’Astous & Ahmed, 1999). An explanation for this can be found in Liu & Johnson
(2005), who prove CoO to also work on a subconscious level. “Consumers’ reluctance
to admit the influence of COO may […] reflect the limitations of their abilities to
discern the sources of influences on their evaluative judgments, rather than that of COO
effects per se” (p. 87). CoO may even influence evaluations and decisions of individuals
originally possessing of enough information to not have to rely on additional sources.
Alba & Hutchinson (1987) hypothesise that simple repetition of tasks may lead to
automatic information processing, even more so when the environment is complex. This
leads to the conclusion of CoO not only bearing enough real-world relevance to allow
for further investigation but probably even being “more powerful than what has
traditionally been thought and detected” (Liu & Johnson, 2005, p. 95).
2.1.2
Country of Origin Image
In order to analyze why consumers prefer products or brands from one country in
comparison to another, emphasis has to be put “on the perceived image of the countries
involved” (Roth & Diamantopoulos, 2008, p. 2), as opposed to the mere presentation of
the CoO cue itself.
For “the individual, the image represents the object, or even is the object” (Jaffe &
Nebenzahl, 2006, p. 14; c.f.: Papadopoulos, 1993). Images bear a high explanatory
power of how people feel and act vis-à-vis certain stimuli, regardless of them being an
actual product, a brand, country, situation or person (Jaffe & Nebenzahl, 2006). They
help in classifying objects and therefore are useful if not even necessary in coping with
the increasing complexity of today’s society (Papadopoulos, 1993) thus bearing high
importance for marketing practitioners (Parameswaran & Pisharodi, 1994; Jaffe &
Nebenzahl, 2006).
In a recent paper, Roth and Diamantopoulos (2008) analysed the different definitions of
country of origin image (CoI) in the literature. They found that the sum of 20 definitions
could be put in three groups: (1) general image of countries, (2) image of countries and
their products and (3) image of products from a country. The first group offers the most
comprehensive view on CoI, it consisting of products as well as for example
9 Literature Review
economical, historical and cultural factors. The image of countries and their products,
the so-called product-country image (PCI) on the other hand is restricted by only
focusing on countries as origin of products. Even though this theory is partly supported
by findings that CoI varies with the product categories in study (e.g.: Bilkey & Nes,
1982; Roth & Romeo, 1992) it still does not capture the whole of the CoI construct. The
third group offers an even more restrictive view, as it focuses only on the products of a
certain country and therefore is much more related to product image (PI) than it is to
CoI (Roth & Diamantopoulos, 2008).
Following image theory, Roth and Diamantopoulos (2008) conclude that an image
should comprise a cognitive (degree of industrial development, political climate, etc.)
(c.f.: Parameswaran & Pisharodi, 1994; Jaffe & Nebenzahl, 2006) as well as an
affective (i.e., emotional) facet (c.f.: Heslop et al., 2008). The conative aspect
(behavioural intention towards the country) is more of an outcome of the other two and
thus should not be part of CoI. Only a few definitions meet the criteria of (1) measuring
the general image of countries and (2) including (only) cognitive and affective facets
(Roth & Diamantopoulos, 2008), the most straightforward being by Verlegh (2001). His
definition of CoI as “a mental network of affective and cognitive associations connected
to the country” (p. 25) will be used in this thesis.
Of ongoing interest to researchers is, how CoI is formed, especially, since images of
places are “not directly under the marketer’s control” (Papadopoulos & Heslop, 2002,
p. 295; c.f.: Parameswaran & Pisharodi, 1994; Kim, 1995). Researchers argue, that it is
based upon “general knowledge about countries picked up everywhere from geography
class […] to daily newspapers and TV documentaries, friends and co-workers […], and
direct experiences from visits to the country” (Heslop & Papadopoulos, 1993, p. 63) and
the consumption of products originating from that country (Niss, 1996; Laroche et al.,
2005; Nadeau et al., 2008; d’Astous et al., 2008). It is widely recognised that the
economic, political, and cultural characteristics bear significant influence on CoI (e.g.:
Wang & Lamb, 1983, Lee & Ganesh, 1999; Jaffe & Nebenzahl, 2006), leading to a bias
against products from low-developed countries (Bilkey & Nes, 1982; Han & Terpstra,
1988; Heslop & Papadopoulos, 1993).
10 Literature Review
2.1.3
Measurement methods of CoI
Roth and Diamantopoulos (2008) further examined the existing measurement scales of
CoI. They identified a total of “30 studies with a concrete measure of country image”
(p. 728) 18 of which are “really different from one another” (p. 733).
One interesting stream of scale development covers the personification of CoI. Chao
and Rajendran (1993) tried to capture CoI by evaluating “consumers feelings towards
COO through their evaluation of individuals who are presumed to own products of
different national origins” (p. 23). They compared the impact, domestic and/or foreign
product ownership had on the image of a college professor vis-à-vis a plant foreman.
This approach is criticized by Nebenzahl et al. (2003), stating “the reference to specific
hypothetical consumer types limit[s] the potential range of responses” (p. 385). In the
same paper they propose a more detailed personification scale. Respondents were asked
to describe a person buying products from a certain country. Since “the country is the
only cue provided to respondents, all attributes reflect back to products made in that
country. Thus, the scale captures not only normative, but also emotional and social
dimensions that consumers attribute to these products” (p. 400). However, Roth and
Diamantopoulos (2008) criticize it as being unclear, which of the above-mentioned
group of country images this scale can be attributed to, as well as whether the scale
really “comprises normative and affective aspects” (p. 734), as the latter is much rather
an outcome of responses. Recently d’Astous and Boujbel (2007) created a scale to
“position countries on human traits” (p. 231). They hypothesise that people have no
difficulty in ascribing personality traits on countries, as has already been shown for
brands (Aaker, 1997). Here again, Roth and Diamantopoulos (2008) criticize the
affective facet to being modelled as an outcome and thus not being part of the scale.
Another group of researchers conducted a large scale study in eight different countries
(US and Europe) in order to link product-country image with the image people hold of
countries and their people (Heslop & Papadopoulos, 1993). The authors partly validated
the model originally used by Nagashima (1977), resulting in a four-factor structure
consisting of Product Integrity, Price/Value, Market Presence and Response.
Parameswaran and his collaborators (e.g.: Parameswaran & Pisharodi, 1994) took the
same path, trying to link product categories with an overall country image. After several
studies, they concluded that CoI consists of general country attributes (divided into an
interaction and a people facet), general product attributes (divided into desirable and
undesirable attributes as well as attributes relating to product image) and specific
product attributes (no clear dimensionality) (Parameswaran & Pisharodi, 1994). Their
results have been validated, amongst others, by Pereira et al. (2005).
Roth & Romeo (1992) proposed a framework for “linking product category perceptions
to country image dimensions” (p. 479). They measured product-country match with
contrasting perceived strength of the country and perceived necessities for the product
category and linking the results to behavioural intention (BeI). They conclude that if “a
country is perceived as having a positive image, and this image is important to a product
category, consumers will be more willing to buy the product from that country”
(p. 493). Whereas on the other hand, using the reference to a country having a positive
image but the respective dimension(s) not being important for the product category,
might not yield in benefits in sales. This approach is being backed by Ittersum et al.
(2003), stating that an “important determinant for the success of regional products is the
match between the product and the region of origin, as perceived by consumers”
(p. 216; c.f.: Ahmed et al., 2004). The results by Veale & Quester (2009), studying the
impact of CoO on the evaluation of wine, Pappu et al. (2006), studying the impact on
TV-sets and cars and Usunier and Cestre (2007), analysing products ranging from cars
to vacuum cleaners to shoes, provide further academic credibility to the framework. The
latter advanced the concept by Roth & Romeo (1992), introducing the so-called product
ethnicity, which measures how strongly a product category is associated with a certain
country and vice versa, i.e., “the degree of product-country match” (Usunier & Cestre,
2007, p. 33).
However, as Roth and Diamantopoulos (2008) show, no scale avoids suffering from at
least one severe methodological drawback. Some show a lack of external validity due to
the use of non-probabilistic samples and/or the lack of cross-country and cross-culture
comparisons. Others lack the report of reliability and/or validity tests, whereas others
don’t mention the origin of their items used. In addition, as with the definition of CoI,
scales vary in which facets measuring the attitude towards the country are included.
Only a few follow the aforementioned image theory including a cognitive and affective
facet. (Roth & Diamantopoulos, 2008)
2.1.4
Country of Origin Effects
Having clarified that CoO “plays a significant role in consumers’ perceptions of
products” (Roth & Romeo, 1992, p. 479), we now focus on the factual impact of the
origin cue on the evaluation of products, services and brands as well as the behavioural
intention towards them.
Over time, CoO research has seen numerous steps forward. The CoI of a certain country
may change over time (Nagashima, 1977; Darling & Wood, 1990; Bilkey, 1993;
Verlegh & Steenkamp, 1999; Nebenzahl et al., 2003), is more important for complex
products (Heslop & Papadopoulos, 1993), higher for more developed countries (Bilkey
& Nes, 1982; Cordell, 1992; Heslop & Papadopoulos, 1993; Verlegh & Steenkamp,
1999; Chinen et al., 2000) and also works in an online-context (Cheng et al., 2008). It
differs by service category (Pecotich et al., 1996), as well as product category (Etzel &
Walker, 1974; Han & Terpstra, 1988; Papadopoulos, 1993; Ittersum et al., 2003), by
CoO and/or nationality of respondent (Cattin et al, 1982; Roth, 1995; Ahmed et al.,
2002; Samiee, 2010). Its’ effect does not differ not between hybrid and non-hybrid
production (Verlegh & Steenkamp, 1999), only slightly between consumers and retail
buyers (Heslop et al., 2004) and is stronger for durable goods (Hsieh et al., 2004). This
knowledge has led to a much better understanding of the phenomenon per se. However,
despite these advances, CoE are still not well understood (Verlegh & Steenkamp, 1999).
Even though the majority of past studies analysed the effect of CoO on product
evaluation (Liefeld, 1993; Leclerc et al., 1994; Peterson & Jolibert, 1995), and “all of
the studies reviewed indicate that country of origin does indeed influence buyers’
perceptions” (Bilkey & Nes, 1982, p. 94; c.f.: Han & Terpstra, 1988), the results on the
amount of impact still differ widely (Ozretic-Dosen et al., 2007), especially in the
presence of other product information cues (Schaefer, 1997). For example, Ahmed et al.
(2002), in their study on consumers’ evaluation of cruise lines, state that in past research
the CoO cue “has been found to explain a relatively small percentage of the variance of
perceived quality [and] attitude, […] suggesting that its theoretical and practical
importance is low” (p. 284). This is partly confirmed by the study of Fong & Burton
(2008), analysing CoO effects in an online environment (discussion groups in the US
and China). They found US consumers to by and large ignore the origin of digital
cameras when being asked to rate or recommend them. Chinese customers on the other
hand, used the origin cue quite extensively, even though most of the according
comments were directed as to boycott Japanese products (see 2.3.1 Consumer
Animosity). The CoO of a product not to be the prevailing cue when evaluating a
product was also found in Veale & Quester (2009) when studying the impact of CoO,
price and taste on the evaluation of wine in Australia. Even though they found that even
when respondents actually tasted the wine, CoO was a more important predictor of
quality ratings than taste (15.08% and 13.1% respectively), both of these factors were
left far behind in importance by price (71.81%). The researchers conclude, “those
tasting the wine allowed their senses to be overcome by their strongly held beliefs in
price and COO, possibly because they mistrusted their own palates” (p. 142), implying
it to be possible, to increase perceived over intrinsic quality by adapting price and CoO.
Johansson et al. (1985), found CoO to not influence overall quality ratings, but only
certain key attributes of the product. In their study, the origin of a car only influenced
the evaluation of gas mileage, horsepower, driving comfort and reliability for some of
the CoOs, whereas no significant influence on overall quality ratings could be detected
in the study.
On the other hand, Josiassen et al. (2008) found CoO to significantly impact product
evaluation and quality perception with a total influence of β3 = 0.43. The results of
Laroche et al. (2005) point in the same direction. They analysed the impact of CoI on
product evaluation for Japan and Sweden for consumers in North America and reported
a total effect (direct and indirect via product beliefs) of r = 0.537 for Japan and 0.548 for
Sweden (the direct effect amounted 0.414 and 0.331 respectively). Another study
showing the origin cue to have a high influence in evaluating products is presented by
Heslop et al. (2004). The researchers analysed the influence of CoI for consumers and
retail buyers in Canada. Five countries were being rated and its influence on product
evaluation and BeI was assessed. The impact of beliefs of products emerging from a
certain CoO on product evaluation was found to be significant and high with
coefficients ranging from β = 0.61 to 0.82. Country evaluations on a general level (e.g.:
trustworthiness, quality of life), on the other hand, showed no significant influence.
In their meta-analysis, Peterson & Jolibert (1995) found an average CoE on product
evaluation of r = 0.16, when taking into account multiple-cue studies only (i.e., studies
that incorporating cues than just CoO). Verlegh & Steenkamp (1999), after analysing 41
3
The standardized beta vaule (β) reflects „the number of standard deviations that the outcome will
change as a result of one standard deviation change in the predictor“ (Field, 2005, p. 193)
studies with 278 effect sizes on CoE between 1980 and 1996, state, “country of origin
has a larger effect on perceived quality than on attitude toward the product” (p. 521)
with an average effect size of r = 0.11 including only multiple cue studies.
As for the effect of CoO on behavioural intention towards a product – the second most
researched dependent variable (Liefeld, 1993; Leclerc et al., 1994; Peterson & Jolibert,
1995) – past research suggests it to be rather low (Erickson et al., 1984; Verlegh &
Steenkamp, 1999; Ahmed et al., 2002). It seems reasonable that, even though a
consumer might think favourably of a certain product, (s)he might still not buy it due to
various reasons, e.g. budget constraints (Peterson & Jolibert, 1995; Verlegh &
Steenkamp, 1999). In other words, even though I do think an Augusta Bell to be a high
quality helicopter, I still won’t buy it due to lack of necessity and money. Roth &
Romeo (1992) reported a positive correlation between CoI and BeI. Their model was
reused by e.g., Wang & Yang (2008), who confirmed their findings with CoI explaining
15.4% of BeI. Wall et al. (1991), found the impact of CoO on likelihood to purchase
manufactured products to be significant but rather unimportant. Josiassen et al. (2008)
studied the evaluation of cars, electronics, watches and electrical household appliances
and found CoO to have an impact on BeI of β = 0.31. Ettenson et al. (1988) reported
fibre content and price to be more important for BeI on apparel than CoO. Peterson and
Jolibert (1995) found an average effect size on BeI of r = 0.19, which dropped to 0.03
when including multiple cue studies only. This is in line with Lim & Darley (1997),
stating single-cue studies to be highly prone to demand effects.
An important point of criticism in past studies is the common usage of fictitious brand
names (e.g.: Peterson & Jolibert, 1995; Ahmed et al., 2002; Veale & Quester, 2009).
Ahmed et al. (2002; 2004) criticize the external validity of this approach, as it doesn’t
reflect a real life situation. Okechuku & Onyemah (1999) propose that their usage may
overestimate CoE “since, in the real world, consumers’ choice sets often include wellknown brands” (p. 620). Furthermore the usage of mere product descriptions is
criticized, e.g. by Peterson & Jolibert (1995), stating that the presence of an actual
product in CoO studies resulted in a decrease of CoE. Liefeld (1993) found no such
(significant) effect, even though the results point in the same direction. Vaela & Quester
(2009) on the other hand, compared the results of respondents having been confronted
with the actual product and those having only received a description and found no
significant differences between those two groups.
2.1.5
Service industry
“There has been tremendous world-wide growth in the services sector, leading to a
substantial increase in economic contribution by services to most national economies”
(Ahmed et al., 2002, p. 284). In Germany, for example, the percentage of employees in
the services industry compared to overall employees has risen from 45% in 1970 to
more than 72% in 2006 (Federal Statistical Office, 2009). The industry is described as
the driving force of economic growth, accounting for 70% of Germanys’ Gross
Domestic Product (GDP) (BmWi, 2010). In Austria 67% of employees are working in
the service industry, with it contributing 70% to GDP (Kugler, 2010).
Already Papadopoulos (1993) stated that CoO-research includes, among other things,
the impact of CoO on products and services (cf.: Bilkey, 1993). However, nearly ten
years later, Ahmed et al. (2002) criticize most previous CoO-research to have focused
primarily on manufactured products (c.f.: Harrison-Walker, 1995; Pecotich et al., 1996;
Jaffe & Nebenzahl, 2006; d’Astous et al., 2008). They hypothesise the importance of
CoO to be higher for the service industry as “purchase and consumption are usually
simultaneous”, resulting in a “higher risk for the consumer" (p. 295; c.f.: Michaelis et
al., 2008). In their study on CoO and brand effects on cruise lines, Ahmed et al. (2002)
found that CoO had an impact of β = 0.268 on product quality and 0.267 on attitude to
product whereas for BeI the impact was 0.167. The results for product evaluation and
BeI significantly exceed the above-mentioned average of 0.16 and 0.03. The researchers
though conclude, “it appears that the interactive effects of CO […] may differ according
to the type of industry: products versus services” (p. 295). Harrison-Walker (1995)
analysed the relevant criteria for choosing an ophthalmologist in the United States and
found provider nationality to significantly influence the selection of a service provider.
The fact that CoO, too, has a significant impact in the service industry, has received
additional support through the study of Pecotich et al. (1996), who analysed CoE on
service evaluation and BeI for banks and airlines. Their results reveal that, if the CoO of
a service provider changes, perception of service quality and BeI, too, undergo
significant changes. Furthermore Bruning (1997) found CoO to play a “significant role
in the selection process for international air carriers” (p. 66) among Canadians with it
being “second only to price in terms of relative importance” (p. 67). Lin & Chen (2006)
analysed the influence of CoI on BeI for insurance and catering services in Taiwan,
concluding it to have a significantly positive influence, even under different product
involvement levels.
Michaelis et al. (2008) studied the impact of CoO on initial trust (i.e. the degree of trust
people award a company or brand they had no prior experience with) for service
providers (telecommunication & insurance). His results reveal that for Polish customers,
CoO has no direct effect on initial trust but enjoys an interaction effect with risk level of
service i.e., services underlying a high-perceived risk (insurance) are subject to higher
initial trust, when the associated CoO conveys a positive image.
2.1.6
Conclusion
We have seen that, despite – or just because of – recent developments, such as reduced
customs requirements or hybrid production, CoO bears significant real-world relevance
(e.g.: Verlegh & Steenkamp, 1999; Liu & Johnson, 2005; Jaffe & Nebenzahl, 2006).
However, due to differences in definition, as well as methodological issues, its’ factual
impact is still not sufficiently understood (Peterson & Jolibert, 1995; Heslop et al.,
2004; Roth & Diamantopoulos, 2008). According to past research, it though seems
reasonable to conclude that direct CoE are stronger for product evaluation than BeI, as
for the latter more predictors (e.g.: price) enter the equation (Baughn & Yaprak, 1993;
Niss, 1996).
“[N]ational images are a given fact and it is up to […] individual firms to deal with
them” (Jaffe & Nebenzahl, 2006, p. 85). Ahmed et al. (2002) describe CoO as
“producer-controlled” strategy (p. 283; c.f.: Cordell, 1992). Han (1989) states,
“individual companies can benefit from favourable country image by selling inferior
products” (p. 228). In other words: it might be described as a question of whether a CoO
fits the companies’ positioning strategy and/or increases its’ sales or not (c.f.: Wang &
Yang, 2008). It is reasonable to assume that, if a company has the possibility of
providing its brand(s) and/or products and services with a favourable CoO – one that in
the long run directly and/or indirectly has a positive influence on its balance sheet – it
will consider to take this opportunity (Jaffe & Nebenzahl, 2006). If, however, expected
results on promoting a certain CoO are neutral or even negative, chances are high of the
company concealing it (Han & Terpstra, 1988; Papadopoulos, 1993). Red Bull refrains
from using any CoO cue whatsoever because it wants to be seen a global brand, Häagen
Dazs benefits from being Danish and Audi as well as Volkswagen clearly communicate
German engineering skills being an integral part of their company, brand and products
(c.f.: Leclerc et al., 1994; Steenkamp et al., 1999; Jaffe & Nebenzahl, 2006).
2.2 CoO and the Brand
It has been made clear that consumers take external cues such as price or CoO into
account when evaluating products and services. Several researchers, too, have included
brand (name & image) into the equation (e.g.: Han & Terpstra, 1988; Wall et al., 1991;
Pecotich et al., 1996; Okechuku & Onyemah, 1999; Ahmed et al., 2002, 2004; Wang &
Yang, 2008), as, since both, CoO and brand are “producer-controlled strategies, the
synergy of their combined inputs is of managerial interest” (Ahmed et al., 2002, p. 283).
In these studies, brand name or brand image4 has been used as another indicator for
quality evaluations. As with other cues, the inclusion of brand image (BI) led the
relative effect of CoO to diminish (Han & Terpstra, 1988; Cordell, 1992). Wall et al.
(1991), for example, found the influence of CoO on the evaluation of manufactured
products to only range from β = 0.002 (for a telephone) to 0.046 (polo shirt) in the
presence of the brand and price cues.
Even though, it seems clear that “country-of-origin effect is most effective when
combined with a strong national brand image” (Pecotich et al., 1996, p. 222; c.f.:
Ahmed et al., 2002), past research is not conclusive on whether CoO or BI bear higher
influence on product evaluation or BeI towards it. D’Astous & Ahmed (1999) compared
the self-assessed importance of CoO (measured as CoM and CoD) to its factual
influence on product evaluation of VCRs among salesmen and consumers in Canada.
When being asked about the importance of CoO, salesmen ranked them as lowest
compared to brand reputation, price and warranty. Consumers, on the other hand,
ranked the two origin-cues as most important ones. However, as the researchers
themselves point out, these results may be highly inflated, as these cues are usually not
“available in a form that [consumers] can use to make an intelligent choice” (p. 119).
They conclude that CoO “becomes an important cue for consumers whenever it is made
available to them at the time of product evaluation” (p. 123). If this is not the case, its’
direct influence is negligible (d'Astous & Ahmed, 1999).
4
defined as „perceptions about a brand as reflected by the brand associations held in consumer memory“
(Keller, 1993, p. 3)
In another study, Ahmed et al. (2002) found CoO to have a stronger effect than brand in
quality evaluation of cruise products, whereas for purchase intention brand shows a
stronger effect. The latter is also supported by Wang & Yang (2008), who studied the
effect of brand personality5 and CoI on BeI towards Sino-German and Sino-Japanese
cars in China. The researchers found brand to explain 21.2% (Sino-German) and 19.8%
(Sino-Japanese) of purchase intention, whereas for CoI the influence amounted 15.4%
and 10.4%, respectively. Hsieh et al. (2004) proposed a model measuring the impact
umbrella-brand images (consisting of Product-, Corporate- and Country-image) are
having on BeI. Their survey of the automobile industry in 20 countries shows that
corporate image and CoO, both part of the overall umbrella-image, bear significant
main effects on purchase behaviour, amounting β = 0.153 and 0.023, respectively.
For product evaluation, Wall et al. (1991) found CoO to have a higher influence on
manufactured products than price and brand. When evaluating cars and TV sets,
according to Okechuku and Onyemah (1999), CoM (β = 0.33 for both) is at least as
important a cue than brand name (0.28 and 0.32, respectively) for Nigerian consumers,
both being more important than price, reliability and safety, all three amounting less
than 0.17. However, Lee & Ganesh (1999) reported brand to have a higher impact than
CoO on consumer evaluations for high-involvement binational brands.
Cordell (1992) found brand to be able to outweigh a negative CoI for products from
less-developed countries (c.f.: Ahmed et al., 2004), whereas this was not the case in
Wall et al. (1991). Furthermore CoI was found not to overcome a negative brand
personality (Wang & Yang, 2008). Interestingly, Lee & Ganesh (1999) found BI and
CoI not to interact with each other. These findings, however, are contradicted by Wang
& Yang (2008), who reported CoI to positively moderate the relationship between brand
and BeI. Furthermore brand influence was found to differ by product category (Wall et
al., 1991; Wang & Yang, 2008).
2.2.1
Brand Image as a Summary Construct
Usunier (2006) states that nowadays brand names are more important for consumers
than CoO. However, as Papadopoulos (1993) mentioned, companies (and thus the
brands) themselves are some sorts of products, which might undergo significant CoE
5
defined as a „set of human characteristics associated with a brand“ (Aaker, 1997, p. 347)
(c.f.: Han, 1989). Verlegh and Steenkamp (1999) undermine this by criticizing the
reliance of CoO-studies on the product and not the brand level. Brands carry various
origin information, communicated via the brand name itself or advertising (e.g.: Thakor
& Lavack, 2003; Kapferer, 2004; Laroche et al., 2005; Samiee et al., 2005; Pappu et al.,
2006) thus probably being significantly influenced by them (e.g.: Baughn & Yaprak,
1993; Chao, 1998; Zeugner-Roth & Diamantopoulos, 2010; Jaffe & Nebenzahl, 2006).
Lee & Ganesh (1999) define a positive CoO to be a “natural requirement” (p. 22) for a
positive BI. Johansson & Nebenzahl (1986) even call the disentanglement of brand and
country “an impossible task in many instances” (p. 103). Brands, such as Microsoft and
McDonald’s may be seen as US diplomats and the Roman Catholic Church as
superbrand (Ham, 2001; c.f.: Paswan & Sharma, 2004). This leads countries to having
their reputation for certain product categories primarily built on the success of certain
brands (Pappu et al., 2006; Usunier & Cestre, 2007). However, these effects have seen
only little attention in scientific literature (Paswan & Sharma, 2004; Samiee et al., 2005;
Jin et al., 2006; Wang & Yang, 2008; Samiee, 2010).
Han & Terpstra (1988) hypothesised, “[c]onsumers utilize extrinsic cues in evaluating a
brand because they often are unable to detect its true intrinsic quality” (p. 236).
Accordingly, Thakor and Kohli (1996) advanced the concept of brand origin, defining
it as “place, region or country to which the brand is perceived to belong by its target
consumers” (p. 27). In other words: Mercedes-Benz is a German brand for cars.
Germany is well known for its expertise in technology and craftsmanship, resulting in a
worldwide reputation for building cars. Thus Mercedes-Benz builds good (excellent)
cars (Thakor & Kohli, 1996; Ham, 2001; d'Astous et al., 2008). Samiee et al. (2005)
identify CoB as “meaningful alternative for bypassing the many conceptual and
research design difficulties and shortcomings associated with C[o]O studies” (p. 393).
In total the concept has received strong support among researchers (e.g.: Lee & Ganesh,
1999; Samiee et al., 2005; Ozretic-Dosen et al., 2007; Koubaa, 2008; Zeugner-Roth &
Diamantopoulos, 2010).
Johansson & Nebenzahl (1986) analysed the impact, a change of production location
might have on two American (Buick & Chevy) and two Japanese car brands (Honda &
Mazda). Following their results, a Honda produced in (West) Germany – if this were
actively communicated – would benefit in terms of stylishness (4.4 to 5.2 on a 7-point
Likert scale), exclusiveness (3.9 to 4.8), whereas loosing in economical to run (5.2 to
4.5) and low service costs (4.2 to 3.9). On the other hand, a change in production
location to the Philippines would result in a severe image-deterioration for all brands.
Thakor and Lavack (2003) studied the effect of location of corporate ownership (CoB)
and location of the source of product (CoM), on perceived brand quality. They reported
the existence of an information hierarchy, where (perceived) CoB bears significant
influence on perceived brand quality, whereas for CoM this is only the case when CoB
information is missing. In their study on the impact of CoO and brand on the evaluation
of banks and airlines, Pecotich et al. (1996) reported a strong interaction effect between
brand and CoO. D’Astous & Ahmed (1999), in their above-mentioned study on
salesmen and consumers in Canada, found their results to “strongly support the
theoretical proposition that brand name serves as a proxy for COO [CoO]” (p. 122).
“Foreign branding can be an effective means of influencing consumers’ perceptions and
attitudes” (Leclerc et al., 1994, p. 269; c.f.: Kim, 1995). Leclerc et al. (1994) and
Zhuang et al. (2008) studied the impact of foreign branding6 on brand image. The
former found products to be perceived as more hedonic for US consumers, when the
(same) brand name was pronounced in French as compared to English. Even when
respondents experienced the products, actual hedonic products still were perceived more
positive, due to their French naming. On the other hand the inclusion of made in France
did not contribute significantly to the evaluations, leading to the conclusion that the
foreign brand name alone communicated the origin-cue well enough (Leclerc et al.,
1994). Zhuang et al. (2008) conducted a similar study in China, concluding that local
brands using foreign brand names were more often judged as being foreign, resulting in
a more positive evaluation.
2.2.2
Brand Origin Recognition
Claiming CoB to significantly influence quality perceptions and BeI via brand image
assumes consumers to have an (correct or incorrect) origin association vis-à-vis the
brand (Paswan & Sharma, 2004; Samiee et al., 2005). If this were not the case, the
importance of CoO might have been inflated in past research (Samiee et al., 2005; see
2.1.3 Relevance of CoI).
6
defined as: “the strategy of spelling or pronouncing a brand name in a foreign language” (Leclerc et al.,
1994, p. 263)
Chao and Rajendran (1993), during their development of a CoI scale, checked whether
US-respondents were able to locate brands they were confronted with in the study. They
were asked to state for 51 brands, which of the 3 countries (USA, Germany and Japan)
they were produced in. Incorrect classification reached from 1.8% (Toshiba – Japan) to
41.2% (Siemens – Germany) with a significant part being falsely classified as domestic
i.e., US brands. On average, misclassification amounted approximately 10% (Chao &
Rajendran, 1993). Paswan & Sharma (2004) analysed the knowledge of brand origin for
four American brands (KFC, McDonald’s, Pepsi and Coke) in India. The correct
classification ranged from 57% (KFC) to 84% (Coke). Furthermore, they found that
travel abroad, socio-economic classification, education and to a certain extent,
familiarity with the US significantly influenced correct classification. The researchers
conclude with advising marketing managers to support better knowledge of brand origin
(Paswan & Sharma, 2004). However, their results may be inflated due to the use of very
well known brands, all of them emerging from the same country (US). In their study of
the influence of CoI on brand equity, Jin et al. (2006) checked for correct country-brand
associations in India and found them to be very high for widespread brands within the
country. However, they note that “[t]he association becomes weaker overtime as the
brands are produced locally” (p. 294). Yasin et al. (2007) state, “[s]ince consumers
today are mostly well educated […], it can be expected that they are well informed
about the original country of their selected brands” (p. 44 f.).
However, in a study on the evaluation of a low-involvement product (food) in Croatia,
Ozretic-Dosen et al. (2007) found respondents to have only minor knowledge of the
origin of the brands in study. Detailed figures are however lacking, which hinders an
extensive interpretation. Zhuang et al. (2008) claimed that consumers in China often are
“confused about the origin of local versus foreign brands” (p. 442), as foreign brands
may choose local sounding names to foster localness. On the other hand, local brands
choose foreign sounding brands to be perceived as Western (and thus hip and modern).
Figures for misjudgement of foreign brands were negatively correlated with product
knowledge and ranged from Nike (1%) to Heineken (53.8%) and – for local brands –
from shoe-producer Li-Ning (3.3%) to casual-wear producer Jasonwood (75.5%,
intentional). Local companies pretending to be foreign thereby profited of the higher
evaluation of (actual) foreign brands (Zhuang et al., 2008). In another study, Samiee et
al. (2005) studied brand origin recognition accuracy (BORA) through a survey
conducted in the United States. A sample of 480 people from all over the US were
asked to classify 84 brands (40 US-brands, 44 International) on which country they
believed them to originate from. Correct brand origin associations amounted only 35%
with 22% for foreign and 49% for US-brands. For respondents with higher brand
familiarity, numbers rose to 33% and 68%, respectively. The number of “don’t know”classifications ranged from 30.1% (England) to 56.4% (Italy) suggesting that
approximately 40-45% of respondents (no results provided for higher familiarity) could
not make any concrete brand origin association at all (Samiee et al., 2005). The study
though, has several shortcomings. Firstly, even though the authors claimed possibly
severe differences between branches, no category-specific analysis has been provided.
Secondly, the don’t-knowers may still be subject to influence of certain regional cues
(i.e.: Anglo-American or European brand). Third the concept of BORA is missing a
measurement of the impact, this lack of (conscious) knowledge is having on the factual
influence of brand origin (Zeugner-Roth & Diamantopoulos, 2010).
These results lead to mixed conclusions about the widespread accuracy of brand origin
associations. D’Astous & Ahmed (1999) found this to be attributable to product
category involvement. In their study, respondents were split into high- and lowinvolvement groups, resulting in significantly higher brand-country matches for the
high-involvement group. In general, past research strongly suggests, that “managers
[…] should periodically monitor the origins associated with their brands” (Samiee et al.,
2005, p. 393) in order to prevent possible negative impacts on their brand due to
(correct or) wrong linkages.
2.3 CoO and the Consumer
Many researchers have studied sociodemographic variables as predictors of product
ownership and/or evaluation (e.g.: Schooler, 1971; Wall et al., 1991; Baughn & Yaprak,
1993; Josiassen et al., 2008; Wang & Yang, 2008). However, even though they are seen
as important moderators (Johansson et al., 1985; Chao & Rajendran, 1993; Baughn &
Yaprak, 1993; Hsieh et al., 2004), very often no significant results or clear pattern of
influence could be detected (Heslop & Papadopoulos, 1993). Heslop & Papadopoulos
(1993) attribute this to the fact that the focus has much rather been on products in
general. Thus every respondent uses his/her own reference point (different types of
products) and arrives at different results, which reduces the probability of detecting
effects attributable to sociodemographic characteristics.
Wall et al. (1991) found older persons to evaluate products higher, whereas Schooler
(1971) found the exact opposite, but only for foreign products. Kapferer (2004) stated
younger people to prefer products of international origin. Johansson et al. (1985)
reported this effect for foreign and domestic products, but only for certain product
attributes. Anderson & Cunningham (1972) on the other hand found no such effect. For
gender Schooler (1971) found females to give higher ratings than males whereas
Johansson et al. (1985) and Baughn & Yaprak (1993) reported mixed results and Wall
et al. (1991) found no significant results at all. Education has been found to positively
moderate product evaluation in Schooler (1971) and Anderson & Cunningham (1972)
and negatively in Wall et al. (1991), whereas Josiassen et al. (2008) found no such
effect. Income was found to be positively correlated with product evaluation in Han &
Terpstra (1988) for all and Jin et al. (2006) for foreign products. Johansson et al. (1985)
found income to have relatively little and Anderson & Cunningham (1972) and
Josiassen et al. (2008) reported it to have no impact.
As opposed to the inconsistent findings concerning sociodemographic characteristics,
CoE have proven to differ by respondent (Cattin et al., 1982; Shimp & Sharma, 1987;
Baughn & Yaprak, 1993; Klein et al., 1998). Jaffe & Nebenzahl (2006) identified four
consumer segments in order to group consumers by their usage of the CoO cue: Patriots,
Cosmopolitans, Traitors and Hostiles. Patriots prefer goods of domestic origin,
downplaying or even ignoring possible image and/or quality differences. Cosmopolitans
are indifferent vis-à-vis origin. Traitors prefer imported goods and Hostiles tend to
boycott goods from certain foreign countries. From this segmentation, the authors
arrived at their Two Dimensional Consumer Segmentation Model (see table 1).
According to Jaffe & Nebenzahl (2006), consumers can be classified by their degree of
ethnocentrism-othercentrism (i.e.: patriot vs. traitor; local vs. foreign goods) and
animosity-affinity (i.e.: hostile vs. friend; attitudes towards a specific country) on a
continuous scale. The concepts of consumer ethnocentrism (CE) and consumer
animosity7 (CA) (and thus their respective counterparts) have shown to be valid in CoO 7
defined as “a consumer’s emotional attachment to the geographic origin of a product.” (Jiménez &
Martìn, 2010, p. 38)
research (e.g.: Shimp & Sharma, 1987; Klein et al., 1998; Ettenson & Klein, 2005; Jaffe
& Nebenzahl, 2006; Riefler & Diamantopoulos, 2007; Jiménez & Martìn, 2010).
Table 1: Two Dimensional Consumer Segmentation Model
Attitude toward
Imports
Attitude toward a country
Animosity
Indifferent
Affinity
1
Conflict
2
Dominated by
Othercentricity
3
Strong attraction to
imports from the
country
4
Dominated by
Animosity
5
No emotional
preference
6
Dominated by
affinity
7
Strong repulsion of
imports from the
country
8
Dominated by
Ethnocentricity
9
Conflict
Othercentricity
Cosmopolitan
Ethnocentricity
Source: Jaffe & Nebenzahl, 2006
2.3.1
Consumer Animosity
Tensions among countries, regions or national groups are generally known to exist all
over the world. They may be due to questions over territory, economy, diplomacy,
religion or mentality (Riefler & Diamantopoulos, 2007). These tensions may translate
into a boycott of brands, products and services based on their CoO (Verlegh &
Steenkamp, 1999; Brodowsky et al., 2004; Heslop et al., 2004). This has been the case,
for example, for products from certain countries in the Middle East and South Asia,
(Heslop et al., 2008). On the other hand, it is being argued that “consumers show a bias
for products from countries with which they have some particular relationships, whether
these relationships are based on geographical proximity, common history, shared
values, shared language, political or economic ties” (d'Astous et al., 2008, p. 382) or
whether they have, for example, emigrated from this country (Jaffe & Nebenzahl,
2006). The former, however, is being contradicted by the results of Riefler &
Diamantopoulos (2007) who, with Austrians expressing animosity towards Germans,
found the construct to be independent of cultural similarity. Their study, though, is
lacking information on whether such animosity based on being wrongly judged as
German and slight differences in mentality translate into product-avoidance.
The fact that animosity may result in serious economical consequences, however, is a
common phenomenon. Klein et al. (1998) conducted the first study in this area,
analysing the effect, negative emotions of Chinese consumers towards Japan are having
on BeI towards Japanese products. They hypothesised that Chinese consumers “might
avoid products […] because the exporting nation [Japan] has engaged in military,
political, or economic acts that a consumer finds both grievous and difficult to forget”
(p. 90). Indeed, their study revealed animosity to be “related negatively to their
willingness to purchase Japanese products” (p. 96) whereas there was no significant
effect on product evaluation (c.f.: Fong & Burton, 2008). However, “[i]f animosity is
sufficiently strong, its effect may be dominant enough for purchase decisions no longer
to be influenced by evaluations of the product” (p. 97).
Ettenson and Klein (2005) analysed the impact French nuclear testing in the South
Pacific had on the evaluation of and intention to purchase French products in Australia.
Their study revealed that this act led to animosity towards France, which resulted in a
de facto consumer boycott of “French firms […], all products perceived to be made in
France, and even enterprises with only a spurious association to France” (p. 202). In
numbers, animosity had an effect of b = -0.59 on willingness to buy. A year later, after
nuclear testing had been stopped, animosity decreased, but still amounted -0.53,
indicating a negative long-term effect. The researchers further found animosity to be
independent of product evaluation during the conflict. In other words: Australians still
regarded French products as being of high quality, but refrained from actual purchase
due to negative attitudes (i.e. animosity) towards France. However, after some time, a
certain denigration of product quality perceptions (-0.26) seemed to occur. The incident
attracted further scientific attention, with Heslop et al. (2008) publishing a longitudinal
study, measuring country-people image effects before, during and after French nuclear
testing (1992, 1995 and 2005, respectively). Their results support the study of Ettenson
& Klein (2005) with product evaluation staying rather stable over time (0.78, 0.84 and
0.82 out of 1, respectively) whereas their influence on BeI suffered intensely during the
incident (dropping from β = 0.67 to 0.48). However, 10 years after nuclear testing had
been stopped, numbers had recovered “even beyond pre-incident attitude levels”
(Heslop et al., 2008, p. 371), thus implying that, even though there is a significant longterm effect, numbers and emotions may balance out at some point.
2.3.2
Consumer Ethnocentrism
As mentioned above, consumer attitudes may also be positive or negative towards
foreign countries, products and services in general. This so-called consumer
ethnocentrism (CE) has been introduced to marketing literature by Shimp & Sharma
(1987), them describing the phenomenon as the “beliefs held by the consumers about
the appropriateness, indeed morality, of purchasing foreign-made products” (p. 280).
Or, as Shankarmahesh (2006) puts it, CE “indicates a general proclivity of buyers to
shun all imported products irrespective of price or quality considerations due to
nationalistic reasons” (p. 147).
Higher levels of CE have found to be the result of a perceived necessity to protect
oneself and one’s in-group (in this case: residents of one’s home country) against the
threat of foreign competition, leading to a preference for local brands, products and
services (e.g.: Bilkey & Nes, 1982; Shimp & Sharma, 1987; Han, 1988; Peterson &
Jolibert, 1995; Verlegh & Steenkamp, 1999) and a higher usage of the CoO cue
(Brodowsky et al., 2004). The degree of CE is positively influenced by factors such as
patriotism, nationalism, salience, out-group size, age, threat of foreign competitors and
conservatism and negatively influenced by factors such as cultural openness, income,
education and capitalism – the strength of influence though, is situation specific
(Balabnis et al., 2001; Shankarmahesh, 2006; Verlegh, 2007).
The phenomenon is being supported by many buy-national campaigns worldwide (e.g.:
Crafted With Pride in U.S.A. in 1985; Buy Russia in the 1990s; Buy Australian Made in
1996; c.f.: Jaffe & Nebenzahl, 2006; Papadopoulos & Heslop, 2002). “Almost every
country has had an industry group, labor union or nation-wide campaign to promote
domestic products” (Jaffe & Nebenzahl, 2006, p. 170). However, Jaffe & Nebenzahl
(2006) note that no evidence has been gathered to support these campaigns for having
significant impact on buying decision, even more so if the superiority proclaimed by the
campaign is not backed by products or services offered (c.f.: Ettenson et al., 1988;
Usunier & Cestre, 2007).
Seeing as a significant influence of CE has been found in virtually every CoO study that
checked for it (except Oretic-Dosen et al. (2007) among young Croatian costumers),
there is a general consensus among the majority of researchers that higher CE leads to
an increase in evaluations for domestic and a denigration concerning foreign products.
In their study, Shimp & Sharma (1987) found respondents with higher levels of CE to
“discount the virtues of foreign-made items” (p. 287). Ettenson & Klein (2005), in their
study mentioned above, reported respondents with higher levels of CE to judge French
products as being of lower quality and experience lower purchase intention. D’Astous et
al. (2008) examined the influence of openness to foreign cultures (OFC) on the
evaluation of art products emerging from 16 different countries. Their results showed
that low OFC (high ethnocentrism) has a “negative impact on the evaluation of foreign
products” (p. 379). These results are backed by Heslop et al. (2008), finding CE to have
a “generalized negative affect towards foreign goods that transcends assessments of
their quality and value” (p. 358).
On the other hand, Balabanis & Diamantopoulos (2004) found CE to be “a more
consistent predictor of preferences for domestic […] rather than for foreign products”
(p. 88), concluding “marketers of foreign products cannot always rely on CE as an
indicator of the likely resistance to their offerings” (p. 91). They found CE to explain
only little variance of respondents’ preference, ranging from 3.3% (for DIY tools) to
8.8% (for TV sets). An explanation for these diverging results can be found in Verlegh
(2007). After having conducted two separate studies, analysing the impact of CE on
evaluation of foreign goods, he concludes, when an industry experiences threat from
abroad, high CE leads to a deduction in the evaluation of foreign companies, products
and services, whereas this is not the case when the local industry is stable. In other
words, it is hypothesised that CE differs by product category (Jaffe & Nebenzahl, 2006;
Verlegh, 2007; d'Astous, et al., 2008).
According to these results, “universal domestic preference is a fallacy” (Heslop &
Papadopoulos, 1993, p. 45). Bruning (1997) found frequent Canadian air travellers to be
“eager to trade off country loyalty for either lower prices or better services offered by
foreign carriers” (p. 69). Brodowsky et al. (2004) analysed US consumers’ evaluation of
cars having either Japan or the US as CoM and/or CoD, finding low ethnocentric
consumers to favour Japanese-designed cars. Shimp and Sharma (1987) provide an
explanation, as “to nonethnocentric consumers, […] foreign products are objects to be
evaluated on their own merits without considerations for where they are made (p. 280).
Especially in developing countries, foreign companies, brands, products and services
often even benefit from their foreignness – enjoying the effect of the so-called
othercentrism (Wang & Lamb, 1983; Cordell, 1992; Okechuku & Onyemah, 1999;
Balabanis & Diamantopoulos, 2004; Jaffe & Nebenzahl, 2006). Okechuku and
Onyemah (1999) studied the evaluation of foreign and homemade goods by Nigerian
consumers. They found the CoO cue to be an important feature of products, because
consumers “want to make sure it is not a domestic brand” (p. 616), resulting in a higher
overall image for European and Asian cars and TV sets. German-made products, for
example, are seen as higher in reliability, technological advancement, prestige and
workmanship compared to their Nigerian counterparts (Okechuku & Onyemah, 1999).
According to Jin et al. (2006), consumers in India prefer products from the UK, as they
are “perceived to be technically advanced, good design, good quality, good reliability[,]
for upper class to be proud of ownership with good appearance [and] good performance
[sic]” (p. 299). Tan & Farley (1987) found consumers in Singapore to prefer goods of
foreign origin. Verlegh (2007) reasoned that, even if there exists a certain home-country
bias, it is not (always) “strong enough to compensate for shortcomings” (p. 363) of the
respective product(s).
2.4 CoO and Familiarity
Familiarity8 is “a crucial element in the information processing model of human
behavior” (Brucks, 1985, p. 1). Past research suggests that consumers with a higher
degree of familiarity vis-à-vis a certain stimuli are able to evaluate it in higher detail
(Alba & Hutchinson, 1987; Shimp et al., 1993; Parameswaran & Pisharodi, 1994;
d’Astous et al., 2008), or, as Papadopoulos (1993) puts it, the evaluation “is likely to
move closer to ‘objective reality’” (p. 6), regardless of whether this shift is positive or
negative (c.f.: Johansson et al., 1985; Ahmed et al., 2002; Heslop et al., 2004). The
degree of familiarity, too, influences, which cues are used to evaluate a product, brand
or service and to what extend this is the case (Rao & Monroe, 1988). It’s exact effect
though, is still unclear (Lee & Ganesh, 1999).
8
defined as “the number of [brand-, country- or] product-related experiences that have been accumulated
by the consumer” (Alba & Hutchinson, 1987, p. 411)
Even though, Usunier (2006) claims studies to often fail checking for respondent
familiarity with all “goods and origins mentioned in the research instrument” (p. 62),
many researchers included familiarity in one way or another in their study (e.g.: Han &
Terpstra, 1988; Wall et al., 1991; Roth & Romeo, 1992; Heslop & Papadopoulos, 1993;
Lee & Ganesh, 1999; Ahmed et al., 2002; Thakor & Lavack, 2003; Paswan & Sharma,
2004; Samiee et al., 2005; d'Astous & Boujbel, 2007).
Lee & Ganesh (1999) reported consumers with low country familiarity to rely more
strongly on the CoO cue in their evaluation of binational brands, whereas Laroche et al.
(2005) found CoO to impact product evaluation regardless of the level of country
familiarity. For brand familiarity, Cordell (1992), studying US-consumers’ evaluation
of watches and shoes from 14 different countries found the importance of the CoO cue
to decline with higher brand familiarity. This view is supported by Brucks (1985)
hypothesizing, “in situations where known brands […] are used, knowledge of the
attribute values of available brands is used as a substitute for more effortful external
search” (p. 12) and Jaffe & Nebenzahl (2006) stating CoI to be just another attribute for
familiar brands. Lee & Ganesh (1999) reached the opposite conclusion, finding
consumers scoring high on brand familiarity to put more emphasis on the CoO cue.
Schaefer (1997) support these findings after studying the evaluation of brands of lager
in 7 countries for English costumers, but only when product class knowledge is high. A
positive correlation between product class familiarity and the usage of the CoO cue has
also been found in Lee & Ganesh (1999), Usunier & Cestre (2007) and is proposed in
Alba & Hutchinson (1987). Knight & Cantalone (2000) reported CoE to be independent
of product class knowledge, whereas Ahmed et al. (2004) hypothesised it to be
negatively related. The results of Josiassen et al. (2008) supported this view with the
researchers concluding it to be “exceedingly important for consumers when they
evaluate products that are associated with product categories they are very unfamiliar
with” (p. 430; c.f.: Cattin et al., 1982).
An explanation for these inconclusive results on the usage of the CoO cue can be found
in Rao & Monroe (1988): “Low-familiar consumers are more likely to use extrinsic
information based on their belief that a quality-extrinsic cue relationship exists in the
marketplace” (p. 262). However, when consumers get more familiar with the country,
and are able to verify (falsify) this relationship, further evaluations are based upon the
knowledge of its (in-)existence. In other words, when consumers arrive at the
conclusion that CoO indeed is a criterion upon which a product can be evaluated, they
will put more emphasis on it. If this, however, is not the case, CoO will be disregarded
in the product evaluation process. (Rao & Monroe, 1988)
2.4.1
Familiarity as a Summary Construct
Until now it has been made clear, that “[...] consumers’ perceptions are formed by
relating to a product [or brand] what they know about a country’s ability to produce
goods and services” (Roth & Romeo, 1992, p. 482). This process, the inferring of
attitudes from one concept (country) to another (product, brand) is called a halo
construct (Alba & Hutchinson, 1987; Han, 1989; Jaffe & Nebenzahl, 2006). Han (1989)
states that past studies have solely relied on this construct in defining CoE. In his study
he found that this, in fact, is true, but only for people of low product familiarity.
Information on the country is transferred to the products or brands in question, making
them similar or even virtually identical in some or all aspects (c.f.: Alba & Hutchinson,
1987). Han (1989) proved, that this transfer might be reversed when consumers are
highly familiar with a nations’ products resulting in them influencing CoI (c.f.: Baughn
& Yaprak, 1993, Schaefer, 1997; Kapferer, 2004). These findings, however, were
contradicted by Knight & Cantalone (2000), Heslop et al. (2004), and Laroche et al.
(2005), who found CoI to impact product evaluations regardless of the level of
familiarity.
Furthermore, Han (1989) treated the constructs as independent of each other with an
occurrence of both when being in the transition phase. Jaffe & Nebenzahl (2006)
analysed past research in this field, concluding that the two may operate simultaneously.
Nebenzahl et al. (1997) created a model where brand, product (service) and country
constantly interact with each other. An experience with a product, service or brand is
influencing the products’ CoO, which, in turn, leads to a revision of the CoI. The
updated image then influences the evaluation and, possibly, intention to buy of brands,
products or services emerging from this country.
31 Research Question, Hypotheses & Model
3. Research Question, Hypotheses & Model
This chapter covers the research question, this thesis is going to answer. Next the
hypotheses based upon the literature review are drawn and the models for the empirical
study presented.
3.1 Research Question
The preceding chapter has shown that the evaluation of and intention to buy brands,
products and services may be significantly influenced by their respective country of
origin (CoO) (e.g.: Heslop & Papadopoulos, 1993; Okechuku & Onyemah, 1999;
Ahmed et al., 2002; Jaffe & Nebenzahl, 2006). Still, Verlegh & Steenkamp (1999)
stated country of origin effects (CoE) to not be well understood and Peterson & Jolibert
(1995) saw the need for additional empirical research on the consequences of CoO
“under a variety of circumstances” (p. 895; c.f.: Parameswaran & Pisharodi, 1994).
Furthermore, examining how CoO influences brand image (BI) should “reveal the
means to protect or enhance the core essence of a brand” (Pappu et al., 2006, p. 697).
Companies (and, of course, public institutions) all over the world and in a variety of
industries are constantly communicating their CoO via their company essentials. One
such example is carrying it embedded in ones’ corporate brand name. For example in
the airline industry (e.g.: British Airways, Austrian Airlines, Emirate Airlines, Air
Berlin) and the financial services industry (e.g.: Deutsche Bank, Bank of America,
Royal Bank of Scotland, Banque Nationale de Paris), carrying an origin indication is not
just an exception to the rule. Through their corporate brand name alone, these
companies are constantly, visibly and prominently communicating their CoO. It is thus
reasonable to assume that they not only experience high CoE, but these effects are
significantly higher compared to companies without an explicit origin indication.
However, to date no study has analysed these effects. Thus the research question of this
thesis is as follows:
Research Question: Does the country of origin effect differ between companies,
bearing their origin in their corporate brand name, compared to companies where this is
not the case?
3.2 Hypotheses & Model
In order to answer above posited research question and to check for the influence of the
constructs and associated phenomenon presented in the literature review, several
hypotheses have to be tested for.
Following CoO-literature, the evaluation of a brand is significantly influenced by the
image of its country of origin (CoI) (e.g.: Chao 1998; Baughn & Yaprak, 1993;
Zeugner-Roth & Diamantopoulos, 2010). Furthermore, according to Papadopoulos
(1993), one can assume that consumers who are more strongly confronted with a
brands’ (desired) CoO, are more affected by it in forming attitudes towards the brand,
the latter thus experiencing higher CoE (c.f.: Pecotich et al., 1996). One possibility to
enhance country associations of a brand is the integration of the CoO in the corporate
brand name (e.g.: Papadopoulos, 1993). Therefore the first hypothesis reads:
H1: CoE are higher for companies, having their origin cue embedded in their
corporate brand name, than for companies, where this is not the case.
CoI, in fact, “may lead to a range of reactions, from simple awareness to attitude
formation to ‘intention to buy’” (Papadopoulos, 1993, p. 22). Past research has
confirmed the positive effect of CoI on both, brand image (e.g.: Johansson &
Nebenzahl, 1986; Thakor & Lavack, 2003) and behavioural intention (BeI) towards
products (e.g.: Roth & Romeo, 1992; Wang & Yang, 2008) Therefore it is
hypothesized:
H2a: CoI has a significant positive effect on BI
H2b: CoI has a significant positive effect on BeI
However, past studies have shown that CoO has higher influence on the evaluation of
brands, products and services, than on intention to buy (Erickson et al., 1984; Peterson
& Jolibert, 1995; Verlegh & Steenkamp, 1999; Ahmed et al., 2002).
H3: CoO has a higher influence on brand image, than on behavioural intention.
CoE have been found to be specific to the product category (e.g.: Etzel & Walker, 1974;
Han & Terpstra, 1988; Papadopoulos, 1993; Ittersum et al., 2003), the image of the
latter thus bearing significant influence on BI (e.g.: Wall et al., 1991; Wang & Yang,
2008). In other words, it is hypothesised that the higher the image of a product category
or industry, the higher BI.
H4: Industry Image (II) has a significant positive effect on BI
Sociodemographic characteristics of respondents (age, gender, income and level of
education) are seen as important indicators of CoE (Johansson et al., 1985; Baughn &
Yaprak, 1993; Chao & Rajendran, 1993; Hsieh et al., 2004). However, comparing
different studies, results were either insignificant or pointing in different directions,
their influence thus remaining inconclusive (Heslop & Papadopoulos, 1993). Still, as
comparable studies have, as to the authors’ knowledge, not yet been accomplished, their
influence on BI and BeI is of interest, even though no directionalities can be
hypothesised.
H5a: Age has a significant influence on BI
H5b: Gender has a significant influence on BI
H5c: Income has a significant influence on BI
H5d: Education has a significant influence on BI
H5e: Age has a significant influence on BeI
H5f: Gender has a significant influence on BeI
H5g: Income has a significant influence on BeI
H5h: Education has a significant influence on BeI
Anderson & Cunningham (1972), in their study, analysed the impact of occupation of
the household head on foreign product preference. Even though he found no significant
effect, occupation is still included in this thesis for the sake of completeness.
H5i: Occupation has a significant influence on BI
H5j: Occupation has a significant influence on BeI
Diverging results, too, have been found for the effect of consumer ethnocentrism (CE)
on the evaluation of brands and BeI towards them (Verlegh, 2007). Most of the
literature though, suggests a negative influence on foreign product evaluation (e.g.:
Shimp & Sharma ,1987; Ettenson & Klein, 2005; d’Astous et al., 2008). On the other
hand, as only Ettenson & Klein (2005) found CE to (negatively) impact purchase
intention, it is hypothesised that CE doesn’t influence BeI.
H6a: CE has a significant negative influence on BI
H6b: CE has no significant influence on BeI
Familiarity has been found to influence both, BI and BeI (Han & Terpstra, 1988;
Liefeld, 1993; Lin & Chen, 2006; Wan & Yang, 2008). However, no consensus among
researchers about the directionality of its general influence has been reached. Even more
so, as familiarity has been hypothesised to strengthen perceived images of the individual
person, rather than per se biasing it in any way (e.g.: Alba & Hutchinson, 1987;
Papadopoulos, 1993; Shimp et al., 1993; d’Astous et al., 2008). It is for these results,
the influence of the diverse familiarities (country, industry and brand) on BI and BeI are
expected to not reach significance. As no relationship between II and BeI is
hypothesised, the effects of industry familiarity on BeI are not tested for.
H7a: The influence of country familiarity on BI is insignificant
H7b: The influence of industry familiarity on BI is insignificant
H7c: The influence of brand familiarity on BI is insignificant
H7d: The influence of country familiarity on BeI is insignificant
H7e: The influence of brand familiarity on BeI is insignificant
Furthermore, past research suggests BI to have a significant and positive influence on
BeI, implying that the higher consumers think of a brand, the more likely they are to
purchase (consumer) it (e.g.: Ahmed et al., 2002; Wang & Yang, 2008).
H8: BI has a significant positive influence on BeI
From these hypotheses, we arrive at the following study-model:
Figure 1: Hypothetical Model for BI and BeI
Consumer
Ethnocentrism
Consumer
Ethnocentrism
Industry Image
Country of Origin
Image
Country of Origin
Image
H2a, pos.
Brand Image
Familiarities (country,
brand, industry)
Familiarities
(country, brand)
Sociodemographics
Sociodemographics
H8, pos.
Behavioural intention
As a further step, the composition of CoI will be analysed and compared with previous
research. For this purpose, the following hypotheses are created:
Even though Balabanis & Diamantopoulos (2004) reported CE to be a less consistent
predictor of products of foreign origin, the majority of past research still found CE to
negatively influence the evaluation of foreign countries (e.g.: Ettenson & Klein, 2005;
d’Astous et al., 2008; Heslop et al., 2008).
H9: CE has a significant negative influence on CoI
Next, as CoE are said to be product-class specific (e.g.: Papadopoulos, 1993), the image
of the industry is hypothesised to positively influence CoI
H10: II has a significant positive influence on CoI
As with BI and BeI, familiarity is said to strengthen country-perception, but not per se
biasing it in a positive or negative way (e.g.: Papadopoulos, 1993).
H11a: The influence of country familiarity on CoI is insignificant
H11b: The influence of industry familiarity on CoI is insignificant
Furthermore, past research on the influence on sociodemographic data is, as already
mentioned, inconclusive. However, e.g., Baughn & Yaprak (1993) define them as
important predictors of CoI. Thus, they are, as above, said to have a direct influence on
CoI, even though no directionalities can be hypothesised.
H12a: Age has an influence on CoI
H12b: Gender has an influence on CoI
H12c: Income has an influence on CoI
H12d: Education has an influence on CoI
H12e: Occupation has an influence on CoI
From these hypotheses, we derive at the following model:
Figure 2: Hypothetical Model for CoI
Consumer
Ethnocentrism
Sociodemographics
Country of Origin
Image
Industry Image
Familiarities
(country, industry)
37 Methodology
4. Methodology
This chapter focuses on the methodology of the empirical study for this thesis. The first
part covers study design, i.e., the companies and countries under study and where the
study has been conducted. Next, the questionnaire development and scales included are
discussed. The following part covers the procedures for data analysis. Last, the sample
description and sampling procedure will be shown and the final study sample described.
4.1 Study Design
In order to answer the studies’ research question and the according hypotheses, a
quantitative study was conducted. Country of origin effects (CoE) were compared
between a company bearing its origin cue in its corporate brand name to one where this
is not the case. In order to achieve a certain amount of comparability and thus
generalizability, both companies had to have same CoO and be active in the same
industry. Furthermore, to reduce possible bias, the two brands to be compared should
both be active on an international market. The study was conducted in two different
countries, in order to obtain a certain international generalizability and follow the everincreasing need for international marketing research (Craig & Douglas, 2005). The
study design is presented in table 2:
Table 2: Study design
Country A
CoO
Industry
Country B
Brand A
Brand B
Following these prerequisites, it was necessary to analyse an industry, where a
significant number of companies carry their origin cue in their corporate brand name.
The financial services industry and Germany proved suitable for this purpose, as in the
former the usage of an origin indication in the corporate brand name is quite common
(c.f.: 3.1 Research Question). Germany, on the other hand, is home of one of the biggest
banks worldwide (Rogers, 2009). Still, it has to be kept in mind that, following the
recent financial crisis, the image of the financial services industry, as well as associated
38 Methodology
brands have suffered in the eyes of the consumer (James, 2009), leading to a possible
lack in cross-industry generalizability of the results.
According to Lim & Darley (1997), Okechuku & Onyemah (1999) and Ahmed et al.
(2002, 2004), real brands were used in the study, in order to obtain more valid results.
Accordingly, Deutsche Bank (DB) and Commerzbank (CB) were selected. Deutsche
Bank currently employs more than 82,000 people in about 72 countries (Deutsche Bank,
2010) and is one of the world’s biggest banks, being second only to Royal Bank of
Scotland in terms of assets (Rogers, 2009). Commerzbank employs over 60,000 people
(with over 30% working outside of Germany) and has, after taking over its competitor
Dresdner Bank, strengthened its position as the countries’ second biggest bank
(Commerzbank, 2010). In terms of Assets, when combining the numbers of both
brands, it currently holds 11th place (Rogers, 2009).
The decision on where to conduct the survey was based upon the fact, in which
countries the respective companies both held branches in the area of private banking at
the time of the study, as this would allow consumers to enjoy a certain familiarity with
both brands. Spain and Italy were found to fulfil these requirements. DB holds more
than 250 branches in both countries, respectively. CB holds two branches in Spain
(Madrid, Barcelona) and Italy (both in Milan), thus restricting the data gathering to
these three cities. Germany was not included in the study, as the according results were
not expected to provide information, necessary for reaching the aims of this thesis.
4.2 Questionnaire Development
The following section provides an overview on the development of the questionnaire. It
will be explained, which scales were used to capture the constructs necessary to answer
our research question and the according hypotheses: country of origin image (CoI),
industry image (II), brand image (BI), behavioural intention (BeI) towards the brands,
brand origin recognition accuracy (BORA), consumer ethnocentrism (CE), familiarity
and sociodemographic characteristics of respondents.
While designing the questionnaire, care was taken to reduce possible demand artefacts
and response bias, while at the same time keeping it at a manageable length. The order
of stimuli was not rotated, as this would have led to a possible overestimation of CoE
(with CoI measured before BI) or CE (with putting an additional emphasis on the
39 Methodology
foreignness of the brands). As in a “well-designed experiment, the interest and purpose
of the researcher is hidden from the subjects” (Liefeld, 1993, p. 118; c.f.: Bilkey & Nes,
1982; Lim & Darley, 1997), consumers were informed, that the purpose of the study is
on globalisation, using the example of the financial services industry. Furthermore, all
measurement scales have been taken from previous country of origin (CoO) studies,
even though some had to be slightly adapted.
The questionnaire was divided into five parts. First, sociodemographic characteristics of
the respondents were gathered. Second consumers’ perception of the financial services
industry (II) was assessed, followed by the evaluation of and intention to buy the
respective brands (BI & BeI). Fourth, the CoO was evaluated (CoI) and lastly,
respondents’ level of CE was assessed.
Sociodemographic characteristics were put first, as they were needed to filter out
respondents who did not fit the quotas (Wilson, 2006). Respondents were asked to
indicate their age (in numbers) and gender (male or female). Personal income of
respondents was assessed in different categories of monthly income (steps of € 500,
starting at € 0 - € 500 and ending at over € 2,500). For profession and education,
respondents were asked to check their current profession and highest completed level of
education out of a list of several alternatives. The measures were constructed on the
broadest categories possible in order to improve comparability (Craig & Douglas,
2005). For profession, the possibility to choose other was included, with asking
respondents, choosing this category, to specify their profession.
The next three parts covered the images of industry, brands and country, respectively.
Due to the fact that the constructs were put into a direct relationship, a single scale, able
to measure all three stimuli was deemed preferable. As only the product-related image
of the CoO was of interest for this thesis, the usage of a method measuring the productcountry image was possible. The scale by Roth & Romeo (1992) proved to be
appropriate for the purpose of this study, even though the lack of an affective facet may
reduce generalizability of results. It was originally developed as to measure the degree
of match between (a) product and (a) country. The researchers analysed past CoOstudies (e.g.: Nagashima 1977; Cattin et al., 1982; Jaffe & Nebenzahl, 1984; Han &
Terpstra, 1988) and identified four common dimensions measuring cognitive production
and marketing attributes of countries and products (innovativeness, design, prestige and
workmanship). As the scale was developed for studying manufactured products, slight
40 Methodology
adaptations were made for it to fit the service industry. Furthermore, a short explanation
was added to clarify the purpose of the question and reduce bias due to diverging
assumption by respondents (e.g.: How would you evaluate the design of services
offered by the banking industry, where design means user friendliness and customer
support?). Attitudes were measured on a 7-point semantic differential scale, where only
the two endpoints of the scale were labelled (e.g.: not appealing and very appealing for
design). This approach allows respondents to take a neutral position if they have no
clear opinion (Wilson, 2006). The evaluation of both brands was assessed
simultaneously, as this approach is said to be closer to real-world situations (Han &
Terpstra, 1988).
All three stimuli were framed by different measures of familiarity, following the need of
covering an appropriate range of experience levels (Alba & Hutchinson, 1987). First,
respondents were asked to self-assess their familiarity with the respective stimuli on a 4item scale, as was done for example in Laroche et al. (2005), Ahmed et al. (2004) and
Josiassen et al. (2008). An unbalanced scale was used, as for familiarity there seemed
no necessity of including a neutral position. Furthermore, the amount of previous
contact with industry, brands and country (4-item scale) was measured. For generating a
clearer picture of the familiarity with the CoO (Germany), another variable, intensity of
previous relations (3-item scale) was included.
For the brands, two further constructs were included. BORA was measured at the
beginning of the brand part, with respondents filling in the respective perceived CoO or,
if they had no such knowledge, indicating they don’t know it. After evaluating the
brands, behavioural intention (BeI) was measured with respondents indicating their
intention to contact the companies, if they were looking for private banking services, on
a scale from 0 to 100 (c.f.: Pecotich et al., 1996).
Consumer ethnocentrism (CE) was assessed last, as to prevent putting special attention
on the foreignness of the two brands. CE was measured using the CETSCALE
developed by Shimp & Sharma (1987). However, as the original version consists of 17
items a shortened 5-item version (c.f.: Steenkamp et al., 1999; Verlegh, 2007) was used,
in order to keep the questionnaire at a manageable length. Respondents’ attitudes were
measured on 5-item Likert scales, ranging from fully disagree to fully agree.
41 Methodology
The questionnaire was first constructed in English. A pre-test with 10 native speakers
showed no necessities for changes. Next the questionnaire had to be translated into
Spanish and Italian. A translation agency was hired to fulfil this task. As special care
has to be taken, when translating constructs (Craig & Douglas, 2005), two natives in
both English and the respective target language checked the final version from the
agency, to reduce possible translation errors. They were briefed about the aims of the
study and given the original questionnaire, in order to ensure correctness of translation.
The final questionnaires can be found in the Appendix (A, B and C; due to the online
gathering of data, the formatting is not visually representative of the online version).
4.3 Data analysis
The data will be analysed in three parts, the preliminary, main and further analysis.
In the first part, the data will be screened and purified from incomplete questionnaires
or those, who are sucseptible of the usage of response patterns. Next, it will be analysed,
whether for the five stimuli (II, BIDB, BICB, CoI and CE) the mean can be used for
further analysis via e.g., Cronbachs alpha, a method of assessing the internal
consistency of a scale (e.g.: Jaffe & Nebenzahl, 1984; Craig & Douglas, 2005). Next,
via comparison of means within countries, it will be analysed, whether for the diverse
familiarity variables, one single variable can be constructed, in order to ease further
analysis. These two steps further provide a basis for the validity of cross-country
comparison of results, as it has to be ensured that differences in ratings by respondents
is attributable to real differences in perceptions and not bias induced by e.g. response
styles (Craig & Douglas, 2005). The next analysis consists of checking the BORA rates
as it has to clarified to what extend respondents are aware of the origin of the brands
(c.f.: Samiee et al., 2005). As a further step, the means of the stimuli will be compared
across the two countries for getting a glimpse on possible intercountry differences.
Furthermore, possible differences between the two brand images within countries will
be checked for, too, via comparison of means within countries. The part closes with a
correlation analysis of all variables but sociodemographic data. This method allows for
the detection of relationships between variables (Field, 2005).
The second part will answer the research question and the acording hypotheses. For this
matter and, according to the two research models presented above (c.f.: 3.2 Hypotheses
42 Methodology
& Model), a multiple regression analysis will be conducted. This method allows for the
analysis as to whether a variable has a significant influence on the dependent variable
(BI, BeI and CoI, respectively). As all our hypotheses have been derived from past
academic literature, the usage of this method will provide us with useful insights on the
statistical significance of influence of variables (e.g.: Field, 2005).
4.4 Sample
„As the adequacy of the sampling procedures affects the generalizability of research
findings, it must be carefully considered in designing origin-related research“ (Baughn
& Yaprak, 1993, p. 103). In order to guarantee for a certain amount of comparability
between the two countries, the respective samples were of equal size and demographic
structure (c.f.: Häubl, 1996). Therefore, the quota sampling method was applied,
resulting in a reduction of generalizability of results, due to the use of a nonprobabilistic sample (Craig & Douglas, 2005; Wilson, 2006). Quotas were set at a
minimum of a third per gender and city (the latter only in Spain) and care was taken to
balance out the sample in terms of age of respondents. The age range was set at 20-49
years, representing a primary target group in the private banking sector. Quotas were set
at a minimum of 20% per age group (20-29, 30-39, 40-49). Levels of education, income
and occupation did not underlie any quotas, thus were expected to vary freely among
respondents. The usage of a student sample was avoided, as the generalizability of such
a sample is not clarified (e.g.: Schooler, 1997, Peterson & Jolibert, 1995). The sample
size was set at 150 respondents per country.
Data was gathered in February 2009 via an online survey, a method growing in
importance and seen as considerably less expensive, even though response rates are
supposed to be rather low (Craig & Douglas, 2005; Wilson, 2006). The latter, though,
may be reduced due to the use of research panels. For this reason, the data has been
gathered with the help of the national research panels of GMR, an international research
agency of the OMNICOM group.
The total sample consisted of 303 respondents. 316 originally started completing the
survey but 13 were refused due to quota issues, leading to a response rate of 95.28 %.
17 questionnaires were excluded, as respondents gave the same ratings for at least three
out of five stimuli, leading to the conclusion of the usage of a response pattern.
43 Methodology
Altogether 286 questionnaires were considered valid for the analysis. The sample
characteristics are shown in table 3.
Table 3: Sample characteristics by country
Spain (n=144)
Frequency
%
Italy (n=142)
Percentage
%
gender
Male
Female
59
85
41.0
59.0
62
80
43.7
56.3
20-29
30-39
40-49
52
52
40
36.1
36.1
27.8
42
52
48
29.6
36.6
33.8
education
Grade School
High School
College (< 2 years)
College (> 2 years)
College degree
Post-graduate degree
13
32
9
14
69
7
9.0
22.2
6.3
9.7
47.9
4.9
6
64
11
12
42
7
4.2
45.1
7.7
8.5
29.6
4.9
occupation
Student
Self-employed
Employed
Unemployed
Other
13
18
97
12
4
9.0
12.5
67.4
8.3
2.8
10
23
91
10
8
7.0
16.2
64.1
7.0
5.6
income
Less than € 500
€ 500 – 1,000
€ 1,000 – 1,500
€ 1,500 – 2,000
€ 2,000 – 2,500
More than € 2,500
12
17
56
26
15
18
8.3
11.8
38.9
18.1
10.4
12.5
22
26
51
25
10
8
15.5
18.3
35.9
17.6
7.0
5.6
age group
The average age of our samples was 33.8 years (Spain, standard deviation: 7.5) and 35.1
years (Italy, standard deviation: 8.3), respectively. In both samples there were more
female than male respondents (Spain: 59.0%; Italy: 56.3%). Most respondents were
employed (Spain: 67.4%; Italy: 64.1%) and earned between € 500 and € 2.000 (Spain:
59.9%; Italy: 71.8%). Respondents checking Other on occupation were mostly
housewives. In both countries, high school and university graduates were most
commonly represented. However, in Italy there were more high school than university
44 Methodology
graduates (45.1% and 29.6% respectively), whereas for Spain the numbers were quite
the opposite (22.2% and 47.9% respectively). Altogether the sample seems to be rather
evenly balanced within and between countries.
45 Results
5. Results
This part focuses on the results of the empirical study mentioned above and the testing
of the respective hypothesis. The chapter starts with a preliminary analysis of the data.
In a next step, the main analysis is conducted, testing for the respective hypotheses and
the research question. The chapter closes with an analysis of the predictors of country of
origin image (CoI), here, too, testing for the respective hypotheses.
5.1 Preliminary Analysis
Prior to running the basic regressions, some initial analyses have been conducted, in
order to get a first insight into the results. This subchapter will conclude with an
analysis of the correlations between the constructs.
5.1.1
Data Screening and Descriptive Statistics
First, the data was screened for outliers due to mistakes in data entry via the use of box
plot diagrams. No such errors could be detected. Furthermore the data did not suffer
from missing values.
Next, the results of the four semantic scales, industry image (II), brand image Deutsche
Bank (BIDB), brand image Commerzbank (BICB) and country of origin image (CoI),
were visualized, in order to obtain first insights into the survey-data. The results are
displayed in figures 3 (Spain) and 4 (Italy).
For the Spanish sample, Germany enjoys the highest image on all four dimensions. The
strengths of the country seem to lie in prestige and quality of associated products and
services. These results are in line with past studies on the image of Germany with the
country (1) enjoying a very favourable image and (2) its strengths lying in prestige and
quality (e.g.: Lillis & Narayana, 1974; Cattin et al., 1982; Shimp et al., 1993). A similar
pattern emerges for Deutsche Bank (DB), even though the company seems to lack of
perceived innovativeness. Some similarities in terms of evaluation can also be spotted
between DB and the financial services industry (II), both having obtained average
46 Results
ratings. Commerzbank (CB) on the other hand, lies behind the other stimuli in every
dimension, even though here, too, prestige and quality obtained slightly higher ratings.
Figure 3: Semantic Differential Scale for Images of Stimuli (Spain)
The results of the Italian sample, as shown below, are of similar nature, even though
images are closer to each other. One interesting finding is the similarity with all stimuli
enjoying the highest ratings on prestige and quality (only exception: quality for industry
in Italy). These results indicate the existence of CoE for both brands. Furthermore, the
higher similarity of ratings between CoI and BIDB suggests higher CoE for this brand,
thus providing principal support for the research question.
Figure 4: Semantic Differential Scale for Images of Stimuli (Italy)
47 Results
As a next step, the five stimuli (CoI, BIDB, BICB, CoI and consumer ethnocentrism (CE))
were transformed into means in order to simplify further analysis. Reliability was
assessed via Cronbach’s alpha. As can be seen in table 4, all values strongly exceed the
recommended value of 0.6 (Craig & Douglas, 2005) and all but one are above 0.8, as
recommended by Field (2005).
Table 4: Cronbach’s Alphas for stimuli
Spain
Italy
II
0.798
0.857
BIDB
0.912
0.899
BICB
0.928
0.921
CoI
0.941
0.902
CE
0.916
0.900
The descriptive statistics of the stimuli, shown in tables 5 (Spain) and 6 (Italy), provide
further insights into the data. Average image for Germany is 4.87 (Spain) and 4.74
(Italy) out of 7, thus by far exceeding the other images. BIDB obtained second highest
ratings with 3.96 and 4.07, respectively, them being located around the scale midpoint.
Average image for the financial services industry amounts 3.76 and 3.83.
Commerzbank, as expected according to the individual results, possesses of the lowest
image of the four, with a mean of 2.98 and 3.44, respectively. Average consumer
ethnocentrism was located below the scale midpoint, amounting 2.44 (Spain) and 2.70
(Italy) out of 5, indicating low to medium ethnocentrism among consumers.
Table 5: Descriptive Statistics of Stimuli (Spain)
Mean
Median
II
3.76
BIDB
3.96
BICB
2.98
CoI
4.87
CE*
2.44
*: measured on a 5-point scale
3.75
4.00
3.25
5.00
2.40
Standard
Deviation
0.98
1.29
1.17
1.23
0.91
Variance
Skewness
Kurtosis
0.96
1.66
1.37
1.51
0.82
0.47
-0.46
-0.43
-0.62
0.18
0.50
0.04
-1.04
0.57
-0.70
Variance
Skewness
Kurtosis
1.61
1.56
1.50
1.23
0.76
-0.68
-0.20
-0.21
-0.52
-0.07
-0.47
-0.24
-0.21
0.50
-0.45
Table 6: Descriptive Statistics of Stimuli (Italy)
Mean
Median
II
3.83
BIDB
4.07
BICB
3.44
CoI
4.74
CE*
2.70
3.88
4.00
3.75
5.00
2.80
Standard
Deviation
1.27
1.25
1.23
1.11
0.87
48 Results
Skewness and kurtosis both deviating from 0 indicate a violation of the assumption of
normally distributed data (Field, 2005). The Kolmogorov-Smirnov and Shapiro-Wilk
tests provide further support for this finding. However, due to the large sample size, this
was not considered problematic for further analysis. Furthermore, the standard deviation
(ranging from 0.87 to 1.29) indicates the mean to reasonably well represent the data.
The descriptive statistics of the diverse familiarity variables (see Appendix D) show a
higher familiarity with all stimuli for Italy. Across countries, respondents reported to be
most familiar with the financial services industry. Both, general familiarity (Spain:
2.57; Italy: 2.56) and previous contact (both: 2.85) are rated only slightly below the
midpoint of the scale (5). Germany enjoys the second highest familiarity with 2.03
(Spain) and 2.16 (Italy) for general familiarity and 1.73 and 2.16 for previous contact.
Previous relations between respondents and Germany, however, only achieved rather
low ratings (Spain: 1.42 out of 3; Italy: 1.33). As for the brands, ratings for DB are
higher than for CB with 1.78 (Spain) and 2.15 (Italy), compared to 1.11 and 1.35
respectively for general familiarity and 1.67 (Spain) and 1.54 (Italy), compared to 1.07
and 1.04 respectively for previous contact. For the diverse familiarity variables,
variance, standard deviation, skewness and kurtosis are akin to the results for the
stimuli. Only for familiarity with CB, results strongly deviate from previous results.
Ratings show a strongly positive skew, and kurtosis goes as high as 38.47 (Spain,
previous contact). In other words, ratings for CB are strongly clustered at the lower end
of the scale.
For behavioural intention (BeI), the results (see Appendix E) indicate a strong
difference between the two brands. For DB, average BeI amounts 42.49 (Spain) and
48.92 (Italy) out of 100, compared to 20.46 and 29.18, respectively for CB. These
results indicate a significantly higher intention to get in contact with DB than CB,
probably for reasons of higher familiarity or a higher influence of CoI. Here again, the
assumption of normal distribution of data could not be confirmed.
5.1.2
Cross-Tabs
In order to be able to draw valid conclusions, it has to be made sure respondents are
aware of the origin of products and brands they are to evaluate (e.g.: Samiee et al.,
49 Results
2005). For this reason, respondents were asked to indicate whether they know the origin
of the two brands and, if yes, to specify it. The results are shown in tables 7 and 8:
Table 7: BORA-rates (Spain)
correct
wrong (total)
wrong (home
country)
total
n = 144
Yes
104
12
9
DB
%
72.22
8.33
6.25
No
-
%
-
Yes
26
2
1
116
80.55
28
19.45
28
CB
%
18.06
1.39
0.69
19.45
No
%
116
80.55
In Spain, more than 80% of respondents indicated to know the origin of DB with 104
respondents (72.22%) having correct origin associations and only 12 respondents
(8.33%) giving a wrong response. For CB the numbers are quite the opposite, with more
than 80% of respondents indicating to have no knowledge of its origin. In total, only
less than 20% knew the origin of CB. The figures of false origin associations are
satisfactorily, with incorrect classifications lying only slightly above 10% for DB and
way below for CB. In other words, if a respondent thinks to know the origin of one of
the two brands, the probability of this association being correct is rather high.
Interestingly, especially for DB the majority of incorrect origin classifications is
attributable to respondents perceiving the brand to be of Spanish origin.
Table 8: BORA-rates (Italy)
correct
wrong (total)
wrong (home
country)
total
n = 142
Yes
128
1
1
DB
%
90.14
0.70
0.70
No
-
%
-
Yes
37
7
-
129
90.84
13
9.16
44
CB
%
26.06
4.93
30.99
No
%
98
69.01
In Italy, a rather similar picture emerges, even though correct associations are higher for
both brands. DB reaches a total of 128 correct classifications (90.14%) with only one
respondent incorrectly classifying the brand. For CB, 37 respondents (26.06%) correctly
identified the brand as of German origin. However, in total nearly 75% of respondents
either did not know the origin at all (69.1%) or provided an incorrect answer (4.93%).
Here again, incorrect classifications were way below 10%.
50 Results
In total, these results show correct origin classification of DB to strongly exceeded
those for CB, indicating a positive recognition effect. It seems reasonable to assume that
this is at least partly attributable to the usage of the origin cue in the corporate brand
name and the higher familiarity, respondents had with DB. Furthermore, the higher
BORA rates for DB indicate higher CoE, thus providing credibility to this thesis’
research question.
5.1.3
Comparison of Means
The equality of images across countries indicated by the results of the descriptive
statistics calculated in the previous subchapter was assessed via independent samples ttests. Results indicate BIDB, BICB, CoI and CE to not significantly differ between the
two countries (see Appendix F). Only for II, a significant difference of means at
t(265,39) = -0.54, p >.01 could be found. The effect size (r = 0.03), however, is very
small, indicating the image of the financial services industry to be significantly, but only
slightly higher in Italy, than in Spain. Furthermore the difference between BIDB and
BICB within the two countries was assessed via a paired samples t-test (see Appendix
G). Results reveal that, as expected, BI for DB was significantly higher at t(143) = 9.84,
p >0.01, r = 0.64 in Spain and t(141) = 7.69, p >0.01, r = 0.54 in Italy, both indicating a
large effect size (Field, 2005).
Concerning the familiarities, paired samples t-tests revealed significant differences
between the diverse types for most of the stimuli (see Appendix H). Only for general
familiarity and previous contact, means were found to be equal for DB and CB in Spain
and for Germany in Italy. For the brands, however, results only reached borderline
significance. These findings prevent the usage of only one familiarity variable as
surrogate for further analysis and are in line with Alba & Hutchinson (1987),
emphasising the usage of different familiarity variables.
Comparison of means of BeI between the two countries (see Appendix I), indicate no
significant difference of ratings. On the other hand, a paired samples t-test between
ratings of the brands within countries (see Appendix J) revealed DB to enjoy a
significantly higher BeI in both, Spain and Italy with t(143) = 10.51, p >0.01, r = 0.66
in Spain and t(141) = 8.33, p >0.01, r = 0.57 in Italy, indicating large effect sizes (Field,
2005). In short, BeI for both brands seems to be equal across countries, with
51 Results
respondents reporting significantly higher intentions to get in contact with DB in both
cases, confirming the results from the previous subchapter.
5.1.4
Correlation Analysis
A correlation analysis was conducted in order to get further insights on the relationships
between all stimuli (CoI, II, BI, CE, BeI and familiarity variables) within countries. For
this purpose, two-tailed Person’s product-moment correlation coefficients were
calculated between all variables. The results can be found in the Appendices K and L.
Altogether, 120 coefficients per country i.e., 240 in total were analysed. 113 (47.1%)
were significant at the .05 level, with 89 (37.1%) reaching significance at the .01 level.
Altogether, 110 (45.83%) coefficients were positive and three negative. Not
surprisingly, all image variables were positively correlated at the .01 level, with
coefficients ranging from 0.262 (BICB and II, Spain) and 0.685 (BIDB and BICB, Italy).
The latter coefficient, too, is very high in Spain (0.527), indicating a close relationship
between the images of the two brands. As for the relationship between BI and CoI, the
image of Germany enjoys higher correlation with BIDB than with BICB in both countries
(Spain: 0.652 for DB and 0.294 for CB; Italy: 0.486 and 0.359). Similar results were
found for II (Spain: 0.454 and 0.262; Italy: 0.579 and 0.515). In general, both industry
and CoO variables seem to correlate higher and through more significant coefficients
with DB than with CB, providing further credence to the research hypothesis.
A significant negative relationship between CoI and CE was found in Spain (-0.235),
but not in Italy. Furthermore CE was negatively related to previous contact with
Germany in Spain (-0.257) and general familiarity with CB in Italy (-0.172). These
results indicate, contrary to our expectations, CE to not influence foreign brand and
country evaluations (c.f.: Balabanis & Diamantopoulos, 2004).
A further contradiction of our expectations was found with nearly all familiarity
variables showing a significant positive coefficient with their respective images. The
only exceptions were II (general familiarity in Italy and previous contact in both
countries) and CoI (previous contact and relations in Italy), who were positive but
didn’t reach significance. These results indicate a strongly positive relationship between
the image of a stimuli and its’ familiarity (i.e., the higher the familiarity with a stimulus,
the higher its’ image). As for the correlation between the different types of familiarities
52 Results
within the respective constructs (e.g.: correlation between all country familiarities), all
showed a significant and positive relationship, with coefficients ranging from 0.287
(industry: general familiarity and previous contact, Italy) and 0.767 (CB: general
familiarity and previous contact, Spain), indicating familiarities within the stimuli to be
positively related. The general familiarity variable with the respective stimuli further
showed positive cross-stimuli correlations, except between country and industry in both
countries, indicating consumers, showing high general familiarity with e.g., Germany to
also be more familiar with brands emerging from this country or industries, this country
is associated with. However, for previous contact, this relationship could be only partly
confirmed.
Correlations between familiarity variables and images of other stimuli provided no clear
patterns. Even though significant and positive coefficients were found for industry and
country familiarity with BIDB in Spain, in Italy no such effect could be detected. Similar
results were found with the familiarity of CB and II as well as familiarity with DB and
CoI. In Italy, only one correlation was found to be significant (familiarity with DB and
II). Here again, results indicate that for the Spanish sample the stimuli are more closely
related than for Italian respondents. These results indicate, familiarities to possess of
certain cross-stimuli correlations. However, no general conclusions can be drawn, as the
results provide no clear patterns.
For BeI, results reveal a close relationship between ratings for both brands (Spain:
0.558; Italy: 0.501). BeIDB was found to be closely and positively related to II, BIDB and
CoI in both countries, with coefficients ranging from 0.281 (II, Italy) to 0.605 (BIDB,
Spain). For BeICB, a different picture emerges, with coefficients of II and CoI not
reaching significance in Italy. Another interesting result is the correlation between BIDB
and BeICB in Spain and BeIDB and BICB in Italy, indicating that, the higher one thinks of
DB (CB), the more likely one, too, considers its German competitor CB (DB) as
possible banking partner. However, these results could not be replicated in the
respective other country.
Familiarity with DB is positively related to BeIDB in both countries. Again, for BeICB,
results are somewhat different. Only general familiarity (in Italy) showed a significant
and positive coefficient with BeICB, whereas in Spain, a relationship with familiarity
with DB was detected. Familiarity with Germany showed a positive relationship to
BeIDB and BeICB only in Spain. Again, ratings for DB seem to be more defined across
53 Results
countries with strong relations to II, BIDB and CoI and general brand familiarity in
Spain and Italy. CB, on the other hand, does not only suffer from lower ratings, but
these ratings seem to only marginally be related to the images of its’ origin, industry
and brand, as well as familiarity with the latter.
All in all, BICB and BeICB seem to be less well defined and to a lesser extent correlated
with other variables, than BIDB and BeIDB. This may be attributable to the lower
familiarity consumers seem to have with CB. Furthermore, both, industry and CoO
seem to be significantly related to BI, strengthening validity to the studies’ hypotheses.
5.2 Main Analysis
The following part will answer the research question and the according hypotheses
posited above. Therefore the impact of the predictors on BI and BeI, according to our
hypothetical model, was assessed via regression analyses.
5.2.1
Regression analysis on BeI
In order to find the statistically significant predictors of BI four regressions (two brands
in two countries) were conducted, according to our study model (c.f.: figure 1 in 3.2
Hypotheses & Model). The regression thus reads:
BIbc = β 1 * CoI + β 2 * II – β 3 * CE +/– β 4 * PCb +/– β 5 * FAMb +/– β 6 * PCc
+/– β 7 * FAMc +/– β 8 * PRc +/– β 9 * PCi +/– β 10 * FAMi +/– β 11 * age
+/– β 12 * gender +/– β 13 * income +/– β 14 * education +/– β 15 * profession + ε
where
BIbc = brand image for brand b in country c
β = standardized beta value
CoI = country of origin image
II = industry image
CE = consumer ethnocentrism
PCb = previous contact with brand b
FAMb = familiarity with brand b
PCc = previous contact with country of origin
54 Results
FAMc = familiarity with country of origin
PRc = previous relations with country of origin
PCi = previous contact with industry
FAMi = familiarity with industry
age = age of respondent
gender = gender of respondent
income = income of respondent
education = education of respondent
profession = profession of respondent
ε = error term of regression
Prior to conducting the regression it was essential to transform the variable profession
into several dummy variables. Afterwards, variables were entered via the blockwise
entry method. The first block included predictors, where a significant impact on the
dependent variable was hypothesised, whereas the second block comprised those
variables, where past literature on their effect was either inconclusive or reported a nonsignificant effect. Next, the analysis was rerun with only significant and borderline
significant variables, as proposed by Field (2005). None of the eight regressions show
signs of multicollinearity, dependent errors and heteroscedasticity, thus drawing of
conclusions is valid (Field, 2005).
The final results of the four regressions on the impact on BI can be found in the
Appendices M to P, a summary is shown in tables 8 and 9. For the sake of readability,
only those results were included that reached significance in at least two of the four
regressions.
Table 9: Regression Analysis on BI (I)
R2
R2 adjusted
DBS*
0.654
0.631
CBS
0.203
0.186
DBI
0.464
0.452
CBI
0.437
0.398
*: Regression on Deutsche Bank in Spain
The goodness of fit indices for all models were significant at p <0.001. Furthermore, the
model seems to describe the data rather well with R2 values ranging from 0.203 (CB,
Spain) to 0.631 (DB, Spain). In other words, between 20 and 63% of variability in BI is
explained by our model, with values being higher in both countries for DB.
55 Results
Table 10: Regression Analysis on BI (II)
Hypothesis
Variable
Brand
B
Standard
error
β
t
Sig
Confirmed?
H2a
CoI
DBS
CBS
DBI
CBI
0.584
0.275
0.336
0.223
0.061
0.073
0.077
0.080
0.557
0.290
0.298
0.202
9.606
3.779
4.365
2.782
0.000
0.000
0.000
0.006
Yes
Yes
Yes
Yes
H4
II
DBS
CBS
DBI
CBI
0.313
0.072
0.238
4.347
0.408
0.422
0.068
0.071
0.415
0.437
5.987
5.927
0.000
n.s.
0.000
0.000
Yes
No
Yes
Yes
0.447
0.181
0.189
2.472
0.470
0.177
0.191
2.657
n.s.
0.015
n.s.
0.009
No
Yes
No
Yes
0.469
0.875
0.339
0.387
0.097
0.225
0.091
0.144
0.267
0.295
0.236
0.196
4.852
3.882
3.731
2.692
0.000
0.000
0.000
0.008
No
No
No
No
H5b
H7c
Rejected:
H5a
H5c
H5d
H5i
H6a
Gender
Brand
Familiarity
DBS
CBS
DBI
CBI
DBS
CBS
DBI
CBI
Age
Income
Education
Occupation
CE
Confirmed (because of non significant results):
H7a
Country
Familiarity
H7b
Industry
Familiarity
On average, six predictors showed to have significant influence on BI. DB in Spain and
CB in Italy are both influenced by nine predictors, whereas for DB in Italy and CB in
Spain, only three variables have reached statistical significance. Only CoI and general
brand familiarity, however, were found to bear significant influence across brands and
countries. Hereby, ratings for the two variables are higher in Spain than in Italy,
indicating a higher importance of the two for Spanish respondents. In Spain, the most
important predictor for BIDB was CoI (β = 0.557), whereas for CB, brand familiarity
had the highest influence (β = 0.295). In Italy, both images were most influenced by the
image of the financial services industry (DB: β = 0.415; CB: β = 0.437).
56 Results
As mentioned above, CoI proves to be a positive and significant predictor of BI with
β-coefficients ranging from 0.202 (CB, Italy) to 0.557 (DB, Spain). This is in line with
previous studies and this thesis’ expectations. Along with H1, the influence is higher for
DB than CB in both countries, indicating the origin cue to bear a higher influence on
companies carrying it in their corporate brand name. If these results can be replicated
for BeI, H1 is validated and the research question answered. Furthermore these results
strongly support H2a, stating CoI to significantly and positively influence BI.
As CoE are widely seen to be product specific, BI was hypothesized to be influenced by
II. In our analysis, the influence of II reached significance in all cases but CB, Spain.
The other three regressions resulted in β-coefficients ranging from 0.238 (DB, Spain) to
0.437 (CB, Italy). H4 thus is partly supported, indicating the image of the respective
industry to positively and significantly affect brand image.
The aforementioned inconclusive influence of sociodemographics (SD) is confirmed by
our results. The influence of education did not reach significance in any of the four
cases, thus rejecting H5d. Age and income only had a significant effect in one of four
cases (age: β = 0.162; income: β = -0.153; both DB Spain), thus rejecting H5a and H5c.
Gender did reach significance in both countries, with females providing higher ratings
(only) for CB (Spain: β = 0.189; Italy: β = 0.191), thus providing limited support for
H5b. As for occupation, no pattern of results could be detected, with students rating DB
higher in Spain (β = 0.122), but CB lower in Italy (β = -0.142), people without current
employment providing lower ratings for DB in Spain (β = -0.119) and CB in Italy
(β = -0.183) and respondents with other occupations rating CB lower in Italy
(β = -0.173). H5i thus is rejected. These results indicate SD to possess of scattered
influence on ratings of BI. However, no clear patterns can be detected, thus preventing
the possibility to forecast ratings based on sociodemographic data.
H6a posited a negative influence of CE on BI based on the brands’ foreign origin. Not
only did our study not reveal such an effect, CE even had a significant positive
influence on BIDB in Spain, (β = 0.154), thus clearly rejecting this hypothesis. These
results indicate that the level of CE of consumers, contrary to the majority of past
research, does not result in a derogation of the image of foreign products/brands.
The influence of the diverse familiarity variables was hypothesised to not significantly
influence BI in any case. In line with our expectations, none of the country-familiarity
57 Results
variables did reach significance, strongly supporting H7a. For familiarity with the
industry, the picture looks similar with previous contact only reaching significance for
BIDB in Spain (β = 0.118) and general familiarity for BICB in Italy (β = -0.164). H7b
thus is partly supported. Familiarity with the brand, on the other hand, seems to bear
significant influence on BI. Even though the variable previous contact did only
significantly predict BICB in Italy (β = 0.171), general familiarity with the brand was
significant across brands and countries. β-values reached from 0.196 (CB, Italy) to
0.296 (CB, Spain) with coefficients being higher in Spain, indicating the variable to
bear significant positive influence on brand image. These results strongly contradict our
expectations, with familiarity said to sharpen images with no clear directionalities. H7c
thus has to be rejected. On the whole though, apart from general brand familiarity, the
influence of familiarity variables on BI provides no clear directionalities, thus
supporting previous literature and our expectations.
5.2.2
Regression analysis on BeI
After analysing the composition of BI, a regression analysis on BeI was conducted.
Here again, four regressions (two brands in two countries) were conducted, according to
our study model (c.f.: figure 1 in 3.2 Hypotheses & Model). The regression thus reads:
BeIbc = β 1 * BIbc + β 2 * CoI – β 3 * CE +/– β 4 * PCb +/– β 5 * FAMb +/– β 6 * PCc
+/– β 7 * FAMc +/– β 8 * PRc +/– β 9 * age +/– β 10 * gender
+/– β 11 * income +/– β 12 * education +/– β 13 * profession + ε
where
BeIbc = behavioural intention towards brand b in country c
BIbc = brand image for brand b in country c
PCb = previous contact with brand b
FAMb = familiarity with brand b
58 Results
The results can be found in the Appendices Q to T, a summary is shown in tables 11 and
12. Again, only variables reaching significance in at least two cases are included.
Table 11: Regression Analysis on BeI (I)
R2
0.407
0.191
0.374
0.158
DBS
CBS
DBI
CBI
R2 adjusted
0.399
0.180
0.360
0.146
Table 12: Regression Analysis on BeI (II)
Hypothesis
H5h
H7e
H8
Rejected:
H2b
H5e
H5f
H5g
H5j
Variable
Education
Brand
Familiarity
BI
Brand
DBS
CBS
DBI
CBI
B
Standard
error
t
-2.785
-3.439
1.363
1.372
-0.140
-0.198
-2.042
-2.507
DBS
CBS
DBI
CBI
9.232
2.919
0.234
3.163
8.471
2.494
0.245
3.397
DBS
CBS
DBI
CBI
11.050
6.462
11.171
6.629
1.659
1.533
1.731
1.684
0.492
0.319
0.464
0.311
6.660
4.215
6.452
3.936
CoI
Age
Gender
Income
Occupation
H6b
CE
H7d
Country
Familiarity
β
59 Sig
Confirmed?
n.s.
n.s.
0.043
0.013
No
No
Yes
Yes
0.002
n.s.
0.001
n.s.
No
Yes
No
Yes
0.000
0.000
0.000
0.000
Yes
Yes
Yes
Yes
Results
Compared to the results for BI, the model for BeI explains less variance with R2 values
ranging between 0.158 (CB, Italy) and 0.407 (DB, Spain). As with BI, values are higher
for DB in both countries, indicating a more thorough composition for BeIDB, probably
attributable to the higher familiarity across both countries. Goodness of fit indices for
all four regressions were significant at p <0.001. Altogether, four variables reached
significance, with only BI influencing BeI across brands and countries.
Contrary to our expectations, CoI did not show any direct effect on BeI in all four cases
at all, thus rejecting H2b. Furthermore, these figures indicate CoI to influence BeI only
via BI, reflecting an indirect effect. Accordingly CoE on BI and BeI are higher for DB
than CB, providing strong support for H1. In other words, our results indicate CoE to be
significantly higher, when the origin of a company is visibly and constantly
communicated by e.g., mentioning it in the corporate brand name.
Furthermore, as CoI has no direct influence on BeI, H3, positing CoI to have a higher
influence on BI than BeI, is validated. These results are in line with past literature,
indicating CoE to be higher for the evaluation of products and brands than for factual
behavioural intention.
Again, sociodemographic characteristics were hypothesised to have a direct influence
on BeI. However, only education was found to reach significance with it negatively
influencing both brands in Italy (DB: β = -0.140; CB: β = -0.198). In other words, the
higher one’s education, the lower one’s intention to get in contact with one of the
brands. H5h thus receives limited support. On the other hand, H5e, f, g and j, (influence
of age, gender, income & occupation) are rejected by our data. Accordingly, results
indicate that sociodemographic variables in general bear only limited influence on both,
BI and BeI.
H6b posited CE not to have any influence on BeI. Results provide strong support for
this hypothesis with the variable not reaching significance in any of the four
regressions. In other words, the tendency of consumers to prefer products, services and
brands of local origin does influence neither evaluation nor intention to buy of their
foreign counterparts, as posited by Balabanis & Diamantopoulos (2004), but much
rather (only) positively affects local products, services or brands. This effect however,
could not be tested for in this study.
60 Results
Furthermore, as for BI, the effect of country and brand familiarity on BeI was
hypothesised to be insignificant. For country familiarity variables, previous relations
with Germany reached significance in one of four cases (β = 0.288; CB, Italy). The
other two variables (general country familiarity & previous contact) didn’t show any
significant effect, supporting H7d. Apart from the indirect effect, general brand
familiarity has via BI, a direct effect for DB in both countries could be detected with βcoefficients amounting 0.234 (Spain) and 0.245 (Italy). H7e thus receives only limited
support. This result may be attributable to the higher familiarity in combination with the
more positive image, DB is enjoying in both countries. In total, however, apart from
general brand familiarity, familiarity variables show no consistent and significant effect
on neither BI, nor BeI.
H8 states BI to positively influence BeI. The regression revealed significant and
positive β-coefficients across brands and countries. Moreover, BI was the most
important predictor in all four cases with coefficients ranging from 0.311 (CB, Italy) to
0.492 (DB, Spain). Once again, β-values are higher for DB in both countries, providing
further support for a more thorough composition of BeI for companies enjoying a better
image and higher familiarity.
5.3 Further Analysis
As an additional step, the influence of diverse variables on CoI was analysed via
multiple regression analysis, according to figure 2 (c.f.: 3.2 Hypotheses & Model). The
regression thus reads:
CoI = β 1 * CE + β 2 * II +/– β 3 * PCc +/– β 4 * FAMc +/– β 5 * PRc
+/– β 6 * PCi +/– β 7 * FAMi +/– β 8 * age +/– β 9 * gender
+/– β 10 * income +/– β 11 * education +/– β 12 * profession + ε
where
II = industry image
61 Results
PCi = previous contact with industry
FAMi = familiarity with industry
The analysis has been computed for both countries and according to the same rules that
have been applied for the main analysis. Again, none of the regressions show signs of
multicollinearity, dependent errors and heteroscedasticity, thus drawing of conclusions
is valid (Field, 2005). The detailed results can be found in the Appendices U and V, a
summary of statistically significant findings is reported in tables 13 and 14.
Table 13: Regression analysis on CoI (I)
Spain
Italy
R2
0.203
0.213
R2 adjusted
0.186
0.202
For both regressions, goodness of fit indices were significant at p <0.001. However, R2
values are rather low with the model only describing 20.3% (Spain) and 21.3% (Italy)
of the variance in CoI. In other words, nearly 80% of influence on CoI is generated by
variables other than those checked for in our model.
Again, the influence of CE does not meet our expectations. H9 posited the variable to
negatively influence CoI. This, however, was only the case in the Spanish sample with
β = -0.208. On the other hand, the influence of CE did not reach significance in Italy.
H9, thus, is only partly supported. These results indicate CE to additionally to not
influencing the evaluation of foreign products, brands and services (c.f.: Balabanis &
Diamantopoulos, 2004), furthermore to not influence the BeI towards them.
H10 posited II to have significantly positive influence on CoI. As in both countries the
variable turned out to be the most important predictor with β-coefficients of 0.250
(Spain) and 0.401 (Italy), this hypothesis receives strong support. These findings are in
62 Results
line with the majority of past research, indicating CoE to be specific to the product
category under study.
Table 14: Regression Analysis on CoI (II)
Hypothesis
Variable
Country
B
Standard
error
β
t
Sig
Confirmed?
H9
CE
Spain
Italy
-0.283
0.104
-0.208
-2.710
0.008
n.s.
Yes
No
H10
II
Spain
Italy
0.313
0.350
0.096
0.066
0.250
0.401
3.267
5.328
0.001
0.000
Yes
Yes
H11a
Country
Familiarity
Spain
Italy
0.346
0.123
0.213
2.826
n.s.
0.005
Yes
No
H12d
Education
Spain
Italy
0.210
0.061
0.265
3.424
0.001
n.s.
Yes
No
Rejected:
H12a
H12b
H12c
H12e
Age
Gender
Income
Occupation
H11b
Industry
Familiarity
As with BI and BeI, familiarity was hypothesised to have no significant effect on CoI.
For CoO, only general familiarity did reach significance in Italy (β = 0.213) and none
of the variables showed a significant effect in Spain. H12a, thus, is supported.
Furthermore, as none of the industry familiarity variables reached significance in either
country, H12b receives strong support. These results again support past research,
indicating familiarity variables to sharpen the respective images but not per se biasing
them in any way.
Last, the influence of SD (age, gender, income, education & occupation) was tested for.
According to the previous regressions, but still contradicting our expectations,
respondents’ characteristics showed no significant and stable influence. Only education
reached significance in one country (β = 0.265, Spain), indicating respondents with
higher education to evaluate the CoO more positively. However, these results only
provide limited support for H13d and H13a-c and e are rejected.
63 Discussion
6. Discussion
In the preceding chapter the analysis of our empirical data has been conducted. On the
following pages we will contrast these findings against past academic literature on
country of origin (CoO). This chapter starts with an introductory part on the purpose
and structure of the study. In the following subchapters the results on the three target
phenomenon, brand image (BI), behavioural intention (BeI) towards a brand and
country of origin image (CoI) are discussed.
Researchers have long posited the magnitude of country of origin effects (CoE) to
depend on whether consumers are in fact aware of the origin of a product or service
(e.g.: Papadopoulos, 1993; Pecotich et al., 1996). Furthermore, it has been stated that
CoO may not only influence ratings of products and services directly but, too, work via
brand origin i.e., the impact of origin associations of consumers on brand image (BI)
(e.g.: Thakor & Lavack, 2003; Kapferer, 2004; Samiee et al., 2005). These origin
indications may, for example, be communicated directly via the brand name with the
use of e.g., a certain language (Lancôme is French, Volkswagen German) or the factual
mentioning of the origin (e.g.: Deutsche Bank, Air India).
The aim of this piece of work was to analyse the influence, embedding an origin cue in
the corporate brand name is having on BI and behavioural intention (BeI) towards the
brand. Therefore a multicountry study has been conducted. We analysed the influence,
CoO is having on the image of and BeI towards two brands emerging from the same
country (Germany) and being active in the same industry (financial services) in two
European countries (Spain & Italy). One brand is carrying its origin indication in its
corporate brand name, whereas for the other brand, no direct indication was present. As
the inclusion of real brands is said to provide more valid results (Lim & Darley; 1997;
Okechuku & Onyemah, 1999; Ahmed et al., 2002, 2004), this approach was adopted for
our study, using Deutsche Bank (DB) and Commerzbank (CB) as exemplary brands.
Following academic literature, several variables influencing CoE, BI and BeI were
included in the analysis in order to test for their impact: industry image (II), consumer
ethnocentrism (CE), familiarity with the country and brand as well as the industry and
sociodemographic characteristics. As CoE are said to be product specific (e.g.: Han &
Terpstra, 1988; Papadopoulos, 1993; Ittersum et al., 2003), the impact of II on BI was
64 Discussion
assessed. The level of CE of the individual consumer was reported to negatively
influence both, BI of and BeI towards foreign brands, products and services, when the
home country is on the same level of development as the CoO (e.g.: Okechuku &
Onyemah, 1999; Verlegh, 2007). As for familiarity, even though familiarity with the
brand, industry and country has been found to influence both, BI and BeI (e.g.: Han &
Terpstra, 1988; Liefeld, 1993), no consensus among researchers about a general
directionality has been reached. We thus tried to prove that familiarity, even though it
strengthens perceived images of stimuli, does not per se exert a positive or negative
influence on CoI, BI and BeI (c.f.: Alba & Hutchinson, 1987; Papadopoulos, 1993;
d’Astous et al., 2008). Furthermore, the influence of sociodemographic characteristics
was assessed, even though, here too, no consensus among the directionality of influence
has been reached in past literature (Heslop & Papadopoulos, 1993).
Our results revealed BI to be significantly and positively influenced by the image of the
brands’ CoO, general familiarity with the brand and II. Furthermore, BeI was found to
be explained by BI and, to a certain extend, general familiarity with the brand (positive
influence) and the education level of respondents (negative influence).
6.1 Composition of Brand Image
The brand of a product or service has long been identified as an important signal for
cues such as price, (service and product) quality, reliability or innovativeness (Kapferer,
2004). Mercedes is a brand for cars of high comfort, high quality but, too, high
acquisition costs. Goldman Sachs is an investment bank striving for profitability at
(nearly) all costs, thus reflecting high market knowledge but probably being rather low
on ethics. Apart from that, Mercedes is intertwined with Germany and, more especially,
German Engineering and Goldman Sachs is the American investment bank. These two
brands are highly associated with their respective CoOs, thus being influenced by their
images. They in fact strongly benefit from their CoOs’ reputation of leading in
engineering (Germany) and being well known for being home of highly profitable and
professional companies (United States). In other words, the image of the CoO
significantly influences the image of brands, being known to emerge from these
countries. Regardless of where a Mercedes is produced, it still is a Mercedes, thus a
high quality vehicle. Put in more academical words, BI does provide consumers with
important information about the products’ or services’ attributes and features. However,
65 Discussion
its composition is not entirely under the marketers control, as the origin, the brand is
associated with, bears significant influence on its image (e.g.: Thakor & Lavack, 2003;
Kapferer, 2004; Samiee et al., 2005).
Our empirical data strongly supports these findings. CoI has been found to positively
and significantly influence BI in all four cases. Consumers from both countries of
analysis (Spain and Italy) used the image of the brands’ CoO (Germany) to evaluate the
two brands under study (Deutsche Bank & Commerzbank). In one case, CoI even was
the most important predictor of BI (β = 0.557).
Several researchers stated it to be necessary for country of origin effects (CoE) to take
place that the consumer is actually aware of the brands’ origin (e.g.: Paswan & Sharma,
2004; Samiee et al., 2005). Additionally, Liu & Johnson (2005) proved CoE to work
independent of the consumers’ willingness to use CoO as a reference. In other words, as
soon as the consumer knows a brands’ CoO, this knowledge automatically influences
his/her evaluation of the very brand. To make use of perceived positive CoE, managers
thus have to make sure that consumers are aware of the brand origin, either via
marketing efforts or even via the brand name itself. Accordingly, the usage of origin
indications in the brand name via e.g. the use of a specific language or the factual
mentioning of the brands’ origin, raises brand origin recognition accuracy (BORA)
among consumers. The latter statement has received strong support in our study. Levels
of correct BORA were three to four times higher for Deutsche Bank than for
Commerzbank, even though the origin indication is in a foreign language. The ratings
for DB (72% in Spain and 90% in Italy) are way above the average for results for
foreign brands found by Samiee et al. (2005) (22%) and comparable those by Paswan &
Sharma (2004) reported for well-known brands such as McDonald’s and Coke.
However, it has to be taken into account that these results may partly be attributable to
the higher familiarity, consumers had with DB. Results for the company not bearing the
CoO in its’ company essentials (CB), proved to be around above-mentioned average for
recognition of foreign brands of 22%. In other words, when a company is constantly
and visibly communicating its’ CoO, consumers are in fact by and large aware of this.
If, however, this information is lacking, recognition is supposedly rather low.
Accordingly, results revealed significant differences in the magnitude of CoE between
the two brands across countries, with difference in β-values quite doubling in one, and
being nearly 50% higher in the other country. Both values outperform the average
66 Discussion
influence of CoO reported in past literature reviews (c.f.: Peterson & Jolibert, 1995;
Verlegh & Steenkamp, 1999), contradicting the findings of Pecotich et al. (1996), who
reported CoE to be low for the evaluation of banks, but supporting the theory of Jaffe &
Nebenzahl (2006), hypothesising CoE to be higher in the service industry. From these
results, it can be concluded that when consumers are aware of a brands’ CoO, the
effects its image is having on the image of the brand is significant and high.
Another fact widely recognized among researchers is the influence of industry image on
the evaluation of a brand (e.g.: Wall et al., 1991; Wang & Yang, 2008). Our results
provide further support, as II was found to be a significant predictor of BI in three out of
four cases. In one country (Italy), it even was the most important predictor for both
brands. In other words, when evaluating a brand, consumers (to a certain extent) use its
image as a proxy of BI. A brands’ industry thus represents yet another cue, consumers
use in evaluating the brand, marketing managers have no direct and instant control of.
Compared to CoI, however, whose influence can be (to a certain extent) in- or
decreased (with putting the CoO in special focus, downplaying, or, in some cases, even
changing it), controlling the influence of ones’ industry is even more difficult, if not
even impossible in many cases.
One factor, which can be controlled for by the company, is the familiarity with ones’
brand. The scientific analysis of directionality and strength of influence of brand
familiarity on BI, however, has not resulted in a consensus among researchers. They
attribute this to the fact that familiarity with a stimulus aids in better evaluating it,
instead of per se having a positive or negative effect (e.g.: Alba & Hutchinson, 1987;
d’Astous et al., 2008). Following this theory, we expected the influence of brand
familiarity to be insignificant. Even though, for all other familiarity variables with the
brand, country and industry, results were indeed insignificant (in at least three out of
four cases), general brand familiarity reached significance in all four cases. In other
words, the more familiar respondents were with either brand, the higher they evaluated
it. It seems reasonable to assume that this may be attributable to the positive image, both
brands enjoyed in the two countries. A possible interpretation for general familiarity
reaching significance with neither industry, nor country, would be the variable of
analysis not being their respective stimuli (but BI). Whether familiarity with a stimulus
may in fact per se result in a more positive evaluation of its very stimulus, however, is
still debatable, as no well-known brand with a weak image has been analysed.
67 Discussion
Another interesting finding is on the influence of CE on BI. According to the majority
of past research, higher levels of CE have two major implications. On the one hand,
they lead to a more favourable evaluation of local products. On the other hand, products
and brands of foreign origin are said to suffer from a derogation of their image.
Depending on the actual difference in product and brand attributes, this may even lead
to consumers’ preferring local products, even though they are of lower quality (e.g.:
Shimp & Sharma, 1987; Ettenson & Klein, 2005). This view is being contradicted by
Balabanis & Diamantopoulos (2004), who reported CE not to be a consistent predictor
for foreign product preference. In other words, higher levels of CE may indeed lead to a
more positive evaluation of local products, but don’t necessarily have a significant
impact on the evaluation of products of foreign origin. Our results provide support to
the latter theory, with CE not negatively influencing BI in any of the four cases. This,
however, may be attributable to the industry specificity of CE, as proposed by Verlegh
(2007). According to his theory, higher levels of CE only lead to a more negative
evaluation of foreign products, services and brands, when the respective local industry
experiences threat from abroad. When consumers perceive the local industry as rather
stable, no changes in evaluation should be expected. Due to the high globalisation in the
financial services industry, it seems reasonable to assume that in this specific industry,
the impact of CE may be lower than in other – more localised – industries.
Additionally, we have studied the influence of sociodemographic characteristics of
consumers, on the evaluation of a brand. According to past research, they are important
predictors, even though previous studies could not reveal consistent patterns throughout
industries. Therefore, in our study, sociodemographics (age, gender, education, income
and occupation) have been hypothesised to influence BI, even though no clear
directionality could be posited. Surprisingly, none of the variables did have a consistent
influence across countries and brands. In fact only three variables had significant
coefficients in more than one case. Females provided higher ratings for CB in both
countries. For DB, however, no such effect could be detected. Students evaluated DB
higher in Spain and CB lower in Italy and people without current employment rated DB
lower in Spain and CB lower in Italy. It can thus be concluded from our data, that
sociodemographic characteristics possess only of scattered influence on BI, thus
preventing to forecast ratings based on them.
68 Discussion
6.2 Composition of Behavioural Intention of Brands
Positive product evaluation does not necessary lead to purchase intention of a product,
brand or service, as other cues, such as price or current need are playing an important
role in this process (Peterson & Jolibert, 1995; Verlegh & Steenkamp, 1999; Heslop et
al., 2004). In the second part of our empirical work, we analysed the impact of BI, CoI,
CE, familiarity and sociodemographic characteristics on BeI, in order to assess their
importance. However, when interpreting these results, it has to be kept in mind that
behavioural intentions are “powerful but imperfect indicators of future purchase
behavior” (Morwitz & Schmittlein, 1992, p. 394; c.f.: Skaggs et al., 1996). Morwitz &
Schmittlein (1992) analysed several marketing studies, finding BeI to both over- and
underestimate actual behaviour, it thus being difficult to forecast consumer actions.
However, even if a high BeI of an individual consumer may not necessarily lead to a
purchase (straight away), its ratings still are of utter importance, as they may “influence
his or her behaviour in general, or that of another individual, immediately or later”
(Papadopoulos, 1993, p. 23).
Previous studies revealed the image of a brand to significantly and positively influence
BeI (e.g.: Ahmed et al., 2002; Wang & Yang, 2008). Our results strongly support this
theory, with BI being a significant predictor of BeI across brands and countries, it even
having the highest β-value in all cases. BI thus is an important factor for consumers,
when establishing behavioural intention towards a brand. In other words, the higher one
thinks of a brand, the more likely one is to take this brand into consideration, when the
need for a product of its’ category arises.
Analysing CoE on BeI is described as elementary in establishing long-term
communication strategies for a brand (Ettenson et al., 1988). Past research has revealed
CoE to be lower for BeI than for evaluation of a product, service or brand (e.g.:
Erickson et al., 1984; Peterson & Jolibert, 1995; Ahmed et al., 2002). In these studies,
significant but rather low direct effects were found. However, in our study, no such
effect could be detected, with CoI not reaching significance in any of the four cases. In
other words, the CoO of a brand does not directly influence behavioural intention
towards the brand. However, as BI affects BeI, so does (indirectly) CoI. Our results thus
show that, even though consumers do not directly use the CoO of a brand when
assessing choice alternatives, due to the fact that CoI significantly influences BI, it, too,
influences BeI, but to a lower extent. As with BI, the impact of CoI is significantly
69 Discussion
higher for DB than for CB. The results for both, BI and BeI, thus strongly support the
hypothesis that, if one company visibly and constantly communicates its’ CoO e.g., via
its’ company essentials such as the corporate brand name, CoE are significantly higher.
The question on whether familiarity variables affect BeI touches the same dilemma as
their influence on BI. In other words, the influence of the diverse familiarity variables
on behavioural intention is supposed to not be measurable i.e., to not reach significance
in either a positive or negative direction. In fact, only one variable did reach
significance in more than one case. Here again it was general brand familiarity, which,
apart from its direct effect on BI, has a direct positive effect on BeI towards DB in both
countries. It seems reasonable to assume that this result may be attributable to the
positive image DB holds in both countries, as well as the relatively high familiarity,
consumers had with it, both lying significantly above ratings for CB.
The influence of CE on BeI, contrary to BI, has been found to be significant only in the
work by Ettenson & Klein (2005). In other words, the (proposed) negative influence of
CE would only reduce BeI via BI. In our study, CE was found to influence neither BI,
nor BeI. Thus, the evaluation and intention to get in contact with a foreign bank is not
influenced by ethnocentric tendencies of consumers, providing further support to abovementioned theory of Balabanis & Diamantopoulos (2004).
Furthermore, as with BI, the influence of sociodemographic characteristics was
assessed, due to researchers positing them to be important predictors (e.g.: Johansson et
al., 1985; Chao & Rajendran, 1993; Hsieh et al., 2004). However, results again do not
support this theory as only education reached significance, with people having a higher
level of education, to have lower BeI for both brands in one country (Italy). From these
results and those of the analysis of BI, it can be concluded that the influence of
sociodemographic characteristics on both, BI and BeI, is scattered and nowhere near a
clear pattern, at least for the financial services industry. Forecasts based solely on them,
thus will not lead to valid results.
6.3 Composition of Country Image
The composition of CoI is of vital importance to both, researchers and marketing
practitioners (e.g.: Parameswaran & Pisharodi, 1994; Papadopoulos & Heslop, 2002). It
is for this reason, we used the available data to validate past results of CoI-research.
70 Discussion
As CoI is said to be industry specific (e.g.: Papadopoulos, 1993), the influence of II on
CoI was assessed. Our results reveal a strong and consistent influence, thus supporting
this theory with the image of the financial services industry significantly affecting CoI
of Germany.
CE was supposed to have a negative influence on CoI (e.g.: d’Astous et al., 2008). This
theory could only be supported for one country (Spain). In Italy, however, no such
effect could be detected, providing further doubt of the validity of the theory stating
consumers with higher ethnocentric tendencies to evaluate foreign products, services or
brands or a foreign country more negatively.
Furthermore the effects of familiarity variables and sociodemographics provide further
support of previous findings. Only the effects of general country familiarity and
education reached significance in one of the two cases. Consumers who were more
familiar with Germany rated the country higher in Italy and respondents with higher
education provided more favourable ratings for Germany in Spain. Familiarity
variables, as well as sociodemographic characteristics, thus once more show no
consistent patterns, providing further credence to above-made conclusion.
71 Conclusion
7. Conclusion
In the last couple of years, criticism on the validity of the country of origin effect (CoE)
has gained support. Researchers argued that, due to diminishing mandatory origin
declaration and the ongoing globalisation, real world relevance of country of origin
(CoO) is decreasing (e.g.: Samiee et al., 2005; Usunier, 2006). This thesis has added
credence to the ongoing importance of the CoO cue, thus contradicting abovementioned criticism. Furthermore, another proof for the validity of CoE in the service
industry has been gathered.
“[P]roducts and services are comprised of a combination of hundreds (perhaps
thousands) of intrinsic and extrinsic cues” (Veale & Quester, 2009, p. 143), one of them
being the brand they are sold under. Recent research has put a special focus on the
effect, country of origin image (CoI) is having via brand image (c.f.: Thakor & Kohli,
1996; Kapferer, 2004; d’Astous et al., 2008; Zeugner-Roth & Diamantopoulos, 2010).
Brand names are said to be globalised in 81% of cases (Kapferer, 2004), and carry
various information of its’ (desired) origin (e.g.: Thakor & Lavack, 2003; Laroche et al.,
2005; Pappu et al., 2006). Several studies proved that, indeed, CoI influences the
evaluation of a brand (e.g.: Leclerc et al., 1994; d’Astous & Ahmed, 1999; Thakor &
Lavack, 2003). Combined with the results of Liu & Johnson (2005), reporting CoE to
subconsciously influence consumer evaluations, it is reasonable to assume that the
power of CoI may even be higher than has been thought.
CoE are said to be higher, the more consumers are (made) aware of the (desired) origin
(Papadopoulos, 1993; Pecotich et al., 1996), with them being insignificant, when
consumers lack such an association (Samiee et al., 2005). For a brand name, such a
strong association may be created by e.g., embedding the origin directly in the corporate
brand name (American Apparel, Russian Standard Vodka, Air India, Deutsche Bank).
The present piece of work has analysed the effect such an indication is having on CoE.
For this purpose, a multinational study on two brands of the same CoO an industry has
been conducted. Deutsche Bank (DB) and Commerzbank (CB), two German brands
active in the financial services industry were selected for analysis. Data has been
gathered in 2009 in two European countries, namely Spain and Italy.
72 Conclusion
Our results provide interesting insights in the functioning of CoE and associated
constructs, such as consumer ethnocentrism (CE). Indeed, it has been shown that a
company, which actively communicates its’ CoO via the brand name, enjoys
significantly higher rates of brand origin recognition accuracy (BORA), i.e., consumers
are more aware of the brands’ CoO. Furthermore the effect, CoI has on BI is
significantly higher across countries. In other words, when a brand is constantly and
visibly communicating it’ CoO, e.g., via carrying it in its’ corporate brand name, (1)
origin associations of consumers are more common and, to a higher extent, correct and
(2) due to this strengthened associations, the impact of CoI on the image of the brand
significantly increases. However, even though CoI directly influences BI, no such effect
could be detected on behavioural intention (BeI). Its’ influence is thus reduced to the
impact it has via BI.
On the other hand, for the effect of industry image (II) on BI, both a direct effect and an
indirect one, via CoI, could be detected. These results are in high accordance with past
research, stating CoI and BI to be specific to the industry (e.g.: Etzel & Walker, 1974;
Wall et al., 1991; Ittersum et al., 2003). The image of a brands’ industry thus is an
important predictor of the formation of the image and, to a lesser extent, intention to
buy (get in contact with) a brand.
Another interesting result has been found in analysing the impact of diverse familiarity
variables on CoI, BI and BeI, respectively. Past research is inconclusive on the impact,
familiarity is having on the evaluation of stimuli. Some academics suggest consumers
with a higher degree of familiarity vis-à-vis a certain stimuli to be able to evaluate it in
higher detail, indicating a possible impact in a positive, as well as negative direction. On
a global level this would indicate familiarity variables to, indeed, aid in evaluating
stimuli but not per se influencing them in a positive or negative way (e.g.: Alba &
Hutchinson, 1987; Papadopoulos, 1993; d’Astous et al., 2008). The present piece of
work supports this theory, with the majority of familiarity variables not showing
consistent influence on either stimulus. Only general familiarity with the brand did
reach significance across brands and countries for BI. For these results to be validated,
however, well-known brands having a weak image have to be analysed.
Furthermore, an analysis on the influence of CE on CoI, BI and BeI, respectively leads
to a strong contradiction of the majority of past research. Contrary to their results, CE
did not have any influence on the evaluation of the CoO, foreign brands and the
73 Conclusion
intention to buy (get in contact with) them. These findings are in accordance with
Verlegh (2007), stating CE to be specific to the industry (higher threat for local industry
leads to a derogation in image of foreign brands and lower BeI) and Balabanis &
Diamantopoulos (2004). The latter found CE to be “a more consistent predictor of
preferences for domestic [...] rather than for foreign products” (p. 88). The influence of
ethnocentric tendencies on the evaluation and BeI of foreign products, services and
brands thus remains unclear and seems to be specific to the situation.
Way more clear are our results on the influence of sociodemographic characteristics on
CoI, BI and BeI. In neither situation, one characteristic did reveal a consistent pattern –
only scattered results could be spotted. Even though past research has identified them as
important moderators on CoE (e.g.: Johansson et al., 1985; Hsieh et al., 2004), no
consistent patterns could be extracted from the sum of their studies either. In accordance
with these results, it can be concluded that sociodemographic characteristics may indeed
influence the individuals’ evaluation of products, services and brands, forecasting these
differences on the basis of their characteristics though is not possible.
7.1 Managerial Implications
“[M]arketing directors are no longer questioning the principle of international
expansion, but are preoccupied with the means by which this can be accomplished”
(Kapferer, 2004, p. 395). Under this condition, it is of vital importance for them to
know how and to what extent, the so-called country of origin effect influences the
associations, consumers have towards their company, brand(s), products and services
(Skaggs et al., 1996; Ahmed et al., 2004).
Country of origin has, in today’s world, become a flexible instrument that can, to a
certain extent, be changed or adapted according to a companies’ positioning strategy
(Papadopoulos, 1993, Kapferer, 2004). However, such moves bear significant long-term
effects, as, when being associated with a certain CoO, “companies must rely on the
behaviors of an entire society or country” (Michaelis et al., 2008, p. 408). Even though,
brands do influence a countries’ CoI, a single brand usually is not in a position to
change it entirely (Jaffe & Nebenzahl, 2006).
If a brand is to be associated with a certain country due to a positive product-country
match and expected positive effects on the brands’ image, it has to clearly communicate
74 Conclusion
this fact. As has been shown in this piece of work, CoE are higher, when consumers are
(made) aware of a brands’ CoO. One way of accomplishing closer associations with a
country is the inclusion of the country in the corporate brand name or other instruments,
elementary to the brand, such as the claim. If negative CoE are to be expected, then
associations with the country should be kept at an absolute minimum and other
associated countries having a more positive image, should be highlighted.
Furthermore, even though a certain amount of familiarity with a brand, country and
industry may be a prerequisite for forming attitudes towards a brand, no per se positive
effects can be expected from higher levels of familiarity. For ethnocentric tendencies of
consumers, too, no general predictions can be made. It is advisable to check for possible
negative effects of CE in ones’ industry in every country, the company is active (or
intends to extend business in) on a regular basis.
In fact, the most straightforward advise, marketing managers should follow can be
found in Kapferer (2004). The French expert in brand management stated, “[e]ach
company has to find its own balance between localisation […] and the deep-rooted
raison d’être of globalisation” (p. 420). In other words, whichever country a brand is
desired to be associated with, the stimuli, it will be judged on (corporate headquarter,
history, brand name, etc.) should be adapted to this very country and actively
communicated. If the aim is to be seen global, then origin associations with whatever
country should be kept at an absolute minimum.
7.2 Limitations and Further research
As every scientific work, this thesis suffers from some limitations, reducing
generalizability of the results. First of all, only two brands in one industry and one CoO
have been analysed. Additionally, the study has been conducted in only two countries.
Furthermore, the CoO, as well as the brands under study possessed of comparatively
high images and familiarity levels. A comparable study including more CoOs, brands
and industries, preferably including some with comparatively lower images and
familiarity levels (e.g.: developing countries) and conducted in other countries would be
desirable in order to validate the findings (c.f.: Jaffe & Nebenzahl, 2006).
75 Conclusion
Another factor biasing our results is the global financial crisis, which started in the fall
of 2008. As data was gathered at its’ peak, consumers evaluation of the financial service
industry and the two brands might have been lower than were to be expected under
normal circumstances (James, 2009). Due to this event the influence of II on BI might
have been over- and the influence of CoI underestimated.
On the methodological side, only a non-probabilistic sample has been used. Second, as
the stimuli in the questionnaire were not rotated, the results might include a certain
order bias, leading to an overestimation of the influence on II on CoI. Third, comparison
of phenomena between countries always bear the risk of taking the difference in
response style as real differences in the evaluation of stimuli (Steenkamp et al., 1999;
Craig & Douglas, 2005). Even though, care has been taken to ensure the validity of
comparison, slight differences might have been undetected. Fourth, employing only
verbal descriptions of products and brands may have resulted in an inflation of effect
sizes (Peterson & Jolibert, 1995). Fifth, the R2-values of the regressions on BeI towards
CB have been rather low, with values being below 0.200, indicating more than 80% of
the variable to be explained by variables other than have been checked for. A more
detailed analysis of BeI would help in determining the exact composition of BeI for
companies, where CoO associations are not that strong. Sixth, the usage of a CoI scale
lacking of an affective facet may have further reduced generalizability of results.
In the future, more research has to be undertaken on the effects of origin indications in
brand names, to validate the results of this thesis and those of Leclerc et al. (1994),
especially in the context of developing countries as stimuli and countries of analysis.
Furthermore, even though recently the amount of CoO research in the service industry
has risen, more studies in this field are necessary in order to detect possible differences
in CoE between products and services (Ahmed et al., 2002; Jaffe & Nebenzahl, 2006).
Additionally, it would be desirable to deepen knowledge on the concept of spontaneous
and subconscious activation of CoE, as brought forward by Liu & Johnson (2005).
76 List of important abbreviations
8. List of important abbreviations
BORA ........................................................................... brand origin recognition accuracy
BeI..................................................................................................... behavioural intention
BeICB .........................................................................behavioural intention Commerzbank
BeIDB ........................................................................ behavioural intention Deutsche Bank
BI .................................................................................................................... brand image
BICB ........................................................................................ brand image Commerzbank
BIDB ....................................................................................... brand image Deutsche Bank
CB ................................................................................................................Commerzbank
CBI.................................................................................................. corporate brand image
CE ................................................................................................ consumer ethnocentrism
CoB ...........................................................................................................country of brand
CoD......................................................................................................... country of design
CoE ................................................................................................ country of origin effect
CoI ................................................................................................ country of origin image
CoO.......................................................................................................... country of origin
DB............................................................................................................... Deutsche Bank
II.................................................................................................................. Industry Image
PCI ................................................................................................ product-country images
77 List of references
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89 Appendices
10. Appendices
Appendix A: Questionnaire (English)
!
1. Introduction
Thank you very much for agreeing on participating in this survey about globalisation using the example of the banking
industry. This study is part of a diploma thesis at the University of Vienna, Department of International Marketing. Filling
out this questionnaire should take no longer than 10 minutes. Please tick the answers that best represent your opinion. There
are no right or wrong answers. The questionnaire is anonymous and no personal data will be stored.
Thank you very much for your participation.
First please let us have some personal information
Please indicate your age
.....
Please indicate your sex
o
male
o
female
In which country do you currently live in?
....
How high is your personal net income per month?
o
! 0-500
o
! 1.000 – 1.500
o
! 2.000 – 2.500
o
! 500-1.000
o
! 1.500 – 2.000
o
Over ! 2.500
What is your current profession?
o
Student
o
Unemployed
o
Self-employed
o
Maternal leave
o
Employed
o
Other (please specify):
What is your highest completed level of education?
o
Grade/Elementary/Jr. High
o
College graduate
o
College (2 years)
o
College (< 2 years)
o
High School/ Secondary
o
Post-graduate degree
2. Part A – Banking Industry
At first we would like to get some information about your overall perceptions of the banking industry
How would you rate your personal familiarity with the banking industry?
"!
90 Appendices
!
Not at all
familiar
Rather
unfamiliar
Rather
familiar
Very
familiar
How would you evaluate the innovativeness of companies and services in the banking industry, where innovativeness means
providing products appropriate to future consumer needs?
Not
innovative
O
Very
innovative
O
O
O
O
O
O
How would you evaluate the design of services offered by the banking industry where design means user friendliness and
customer support?
Not
appealing
O
Very
appealing
O
O
O
O
O
O
How would you evaluate the prestige of companies and services in the banking industry, where prestige means exclusivity,
status, brand name reputation and variety of services?
Not
prestigious
O
Very
prestigious
O
O
O
O
O
O
How would you evaluate the reliability and quality of services offered by the banking industry?
Not
reliable
O
Very
reliable
O
O
O
O
How often are you in contact with your bank?
Every 6 months
or less often
Every 2-3
months
Every
month
Every week
or more often
"!
91 O
O
Appendices
!
3. Part B – Brands
Next we would like to ask you some questions about your personal opinion of two banks, Deutsche Bank and
Commerzbank.
Do you know the brands’ respective country of origin?
If yes, please indicate the
country below
Don’t know
(please tick)
Deutsche Bank
Commerzbank
Have you ever had private or professional contact with these brands?
No, never
Yes, once
Yes, 2-3 times
Yes, more
than 3 times
Deutsche Bank
Commerzbank
How would you rate your personal familiarity with these brands?
Not at all
familiar
Rather
unfamiliar
Rather
familiar
Very
familiar
Deutsche Bank
Commerzbank
How would you evaluate the innovativeness of the two brands, where innovativeness means providing products appropriate
to future consumer needs?
Not
innovative
Very
innovative
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
How would you evaluate the design of the two brands, where design means appearance and style?
Not
Very
"!
92 Appendices
!
appealing
appealing
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
How would you evaluate the prestige of the two brands, where prestige means exclusivity, status, brand name reputation and
variety of services?
Not
prestigious
Very
prestigious
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
How would you evaluate the reliability and quality of the two brands?
Not
reliable
Very
reliable
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
If you were searching for a bank for private banking purposes, what is the likelihood of selecting each of these companies (0
= never, 100 = I would definitely contact the bank)?
Number of points
Deutsche Bank
...
Number of points
Commerzbank
...
4. Part C – Germany
Next we would like to ask you a few questions about your personal perceptions of Germany
Have you ever been to Germany?
No, never
Yes, once
Yes, 2-3 times
Yes, more than 3 times
"!
93 Appendices
!
Do you have any private or professional relations to Germany?
No relations
whatsoever
Rather loose
relations
Intense
relations
How would you rate your personal familiarity with Germany?
Not at all
familiar
Rather
unfamiliar
Rather
familiar
Very
familiar
How would you evaluate innovativeness of German products and services, where innovativeness means providing products
appropriate to future consumer needs?
Not
innovative
O
Very
innovative
O
O
O
O
O
O
How would you evaluate the design of German products and services, where design means appearance and style?
Not
appealing
O
Very
appealing
O
O
O
O
O
O
How would you evaluate the prestige of German products and services, where prestige means exclusivity, status, brand name
reputation and variety of services?
Not
prestigious
O
Very
prestigious
O
O
O
O
O
O
How would you evaluate the reliability and quality of German products and services?
Not
reliable
O
Very
reliable
O
O
O
O
"!
94 O
O
Appendices
!
5. Part D – Personal Views
Last we would like to ask you some questions on your personal views regarding imports
Spanish / Italian people should not buy foreign products, because this hurts Spanish / Italian business and causes
unemployment
Fully
disagree
Rather
disagree
Neither agree
nor disagree
Rather agree
Fully agree
It is not right to purchase foreign products, because this puts Spanish / Italian people out of jobs
Fully
disagree
Rather
disagree
Neither agree
nor disagree
Rather agree
Fully agree
A real Spain/Italian should always buy Spanish / Italian products.
Fully
disagree
Rather
disagree
Neither agree
nor disagree
Rather agree
Fully agree
I always prefer Spanish / Italian products over foreign products.
Fully
disagree
Rather
disagree
Neither agree
nor disagree
Rather agree
Fully agree
We should purchase products manufactured in Spain / Italy, instead of letting other countries get rich off us.
Fully
disagree
Rather
disagree
Neither agree
nor disagree
Rather agree
Fully agree
"!
95 Appendices
Appendix B: Questionnaire (Spanish)
1. Introducción
Muchas gracias por acceder a participar en esta encuesta sobre la globalización en la que utilizaremos el sector de los
servicios financieros como ejemplo. Esta encuesta es parte de una tesis para la obtención de un diploma en la Universidad de
Viena, Departamento de Marketing Internacional. Completar este cuestionario no le llevará más de 10 minutos. Por favor,
marque las respuestas que representan mejor su opinión. No hay respuestas correctas o incorrectas. El cuestionario es
anónimo y no se almacenará ningún dato personal.
Muchas gracias por su participación.
A continuación le vamos a pedir algunos datos personales
Por favor, indique dónde vive actualmente.
Para el cuestionario en España:
o
Barcelona
o
Valencia
o
Alicante
o
Madrid
o
Sevilla
o
Otra ciudad
Por favor, indique su edad.
.....
Por favor, indique su sexo
o
hombre
mujer
o
Por favor, indique el nivel de sus ingresos netos mensuales.
o
0-500 !
o
1.000 – 1.500 !
o
2.000 – 2.500 !
o
500-1.000!
o
! 1.500 – 2.000 !
o
Más de 2.500 !
Por favor, indique a qué se dedica actualmente.
o
Estudiante
o
Desempleado
o
Trabajador autónomo
o
o
Empleado
De baja por
maternidad/paternidad
o
Otra ocupación (por favor,
especifique)
¿Cuál es el nivel de educación más alto que ha completado?
!
o
Educación
primaria/secundaria
(obligatoria)
o
Educación secundaria
(bachillerato)
o
Universidad (< 2 años)
o
Universidad (2 años)
"!
96 o
Licenciatura o
diplomatura
universitaria
o
Postgrado
Appendices
2. Parte A - Industria bancaria
En primer lugar, necesitaríamos tener alguna información sobre sus percepciones generales respecto a la industria de
servicios financieros
¿Cómo clasificaría lo familiarizado que está con la industria bancaria?
Nada
familiarizado
Poco
familiarizado
Bastante
familiarizado
Muy
familiarizado
¿Cómo evaluaría el grado de innovación de las empresas y servicios de la industria bancaria, si por innovación entendemos
suministrar productos adecuados a las necesidades futuras de los consumidores?
Nada
innovadores
O
Muy
innovadores
O
O
O
O
O
O
¿Cómo evaluaría el diseño de los servicios de la industria bancaria, si por diseño entendemos aspecto y estilo?
Nada atractivos
O
Muy atractivos
O
O
O
O
O
O
¿Cómo evaluaría el prestigio de las empresas y servicios de la industria bancaria, si por prestigio entendemos exclusividad,
estatus, reputación de la marca y variedad de servicios?
Nada
prestigiosos
O
Muy
prestigiosos
O
O
O
O
O
O
¿Cómo evaluaría la fiabilidad y la calidad de los servicios de la industria bancaria?
Nada fiables
O
Muy fiables
O
O
O
O
¿Con qué frecuencia se pone en contacto con su banco?
Cada 6 meses o con
menos frecuencia
!
Cada 2-3
meses
Todos los
meses
Todas las semanas o
con más frecuencia
"!
97 O
O
Appendices
3. Parte B - Marcas
Ahora nos gustaría hacerle algunas preguntas para conocer su opinión personal acerca de dos bancos, Deutsche Bank
y Commerzbank.
¿Conoce el país de origen de cada una de estas marcas?
Si lo conoce, por favor,
indique el país a continuación
No lo conozco (por
favor, marque)
Deutsche Bank
Commerzbank
¿Alguna vez ha tenido contacto con estas marcas, ya sea de forma privada o por motivos laborales?
No, nunca
Sí, una vez
Sí, 2-3 veces
Sí, más de 3 veces
Deutsche Bank
Commerzbank
¿Cómo clasificaría lo familiarizado que está con estas marcas?
Nada
familiarizado
Poco
familiarizado
Bastante
familiarizado
Muy
familiarizado
Deutsche Bank
Commerzbank
¿Cómo evaluaría el grado de innovación de los dos bancos, si por innovación entendemos suministrar productos adecuados a
las necesidades futuras de los consumidores?
Nada
innovador
Muy
innovador
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
!
98 Appendices
¿Cómo evaluaría el diseño de las dos marcas, si por diseño entendemos aspecto y estilo?
Nada
atractivo
Muy
atractivo
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
¿Cómo evaluaría el prestigio de las dos marcas, si por prestigio entendemos exclusividad, estatus, reputación de la marca y
variedad de servicios?
Nada
prestigioso
Muy
prestigioso
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
¿Cómo evaluaría la fiabilidad y la calidad de las dos marcas?
Nada fiable
Muy fiable
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
Si estuviese buscando un banco para sus fines personales, ¿qué probabilidades habría de que escogiese cada una de estas
empresas (0 = nunca, 100 = Sin duda me pondría en contacto con el banco)?
Número de puntos
Deutsche Bank
...
Número de puntos
Commerzbank
...
4. Parte C - Alemania
Ahora nos gustaría hacerle algunas preguntas para conocer su percepción personal sobre Alemania
!
99 Appendices
¿Ha estado en Alemania alguna vez?
No, nunca
Sí, una vez
Sí, 2-3 veces
Sí, más de 3 veces
¿Tiene alguna relación personal o profesional con Alemania?
Ninguna
relación
Relaciones
ocasionales
Relaciones
intensas
¿Cómo clasificaría lo familiarizado que está con Alemania?
Nada
familiarizado
Poco
familiarizado
Bastante
familiarizado
Muy
familiarizado
¿Cómo evaluaría el grado de innovación de los productos y servicios alemanes, si por innovación entendemos suministrar
productos adecuados a las necesidades futuras de los consumidores?
Nada
innovadores
O
Muy
innovadores
O
O
O
O
O
O
¿Cómo evaluaría el diseño de los productos y servicios alemanes, si por diseño entendemos aspecto y estilo?
Nada
atractivo
O
Muy
atractivo
O
O
O
O
O
O
¿Cómo evaluaría el prestigio de los productos y servicios alemanes, si por prestigio entendemos exclusividad, estatus,
reputación de la marca y variedad de servicios?
Nada
prestigiosos
O
Muy
prestigiosos
O
O
O
O
100 O
O
Appendices
¿Cómo evaluaría la fiabilidad y la calidad de los productos y servicios alemanes?
Nada fiables
O
Muy fiables
O
O
O
O
O
O
5. Parte D – Opiniones personales
Ahora nos gustaría hacerle algunas preguntas para conocer su opinión personal respecto de las importaciones
Los españoles/italianos no deberían comprar productos extranjeros, porque ello lesiona los negocios españoles/italianos y
genera desempleo
Totalmente en
desacuerdo
Bastante en
desacuerdo
Ni de acuerdo ni
en desacuerdo
Más bien de
acuerdo
Totalmente
de acuerdo
No es correcto comprar productos extranjeros, porque esto hace que los españoles/italianos pierdan sus empleos
Totalmente en
desacuerdo
Bastante en
desacuerdo
Ni de acuerdo ni
en desacuerdo
Más bien de
acuerdo
Totalmente
de acuerdo
Un verdadero español/italiano siempre debería comprar productos españoles/italianos.
Totalmente en
desacuerdo
Bastante en
desacuerdo
Ni de acuerdo ni
en desacuerdo
Más bien de
acuerdo
Totalmente
de acuerdo
Siempre prefiero los productos españoles/italianos en lugar de los productos extranjeros.
Totalmente en
desacuerdo
Bastante en
desacuerdo
Ni de acuerdo ni
en desacuerdo
Más bien de
acuerdo
Totalmente
de acuerdo
Deberíamos comprar productos fabricados en España/Italia, en lugar de dejar que otros países se enriquezcan a nuestras
expensas.
Totalmente en
desacuerdo
Bastante en
desacuerdo
Ni de acuerdo ni
en desacuerdo
Más bien de
acuerdo
!
101 Totalmente
de acuerdo
Appendices
Appendix C: Questionnaire (Italian)
1. Introduzione
La ringraziamo di aver consentito di far parte a questo sondaggio sulla globalizzazione, il quale utilizza l’esempio
dell’industria dei servizi finanziari. Il presente studio fa parte di una tesi di laurea stesa presso l’Università di Vienna,
Dipartimento di Marketing Internazionale. La compilazione del questionario non dovrebbe richiedere più di 10 minuti. Spunti
le risposte che meglio rappresentano la Sua opinione. Non vi sono risposte giuste o sbagliate. Il questionario è anonimo e
non verrà memorizzato alcun dato personale.
Grazie per la Sua partecipazione!
La preghiamo ora di fornirci alcuni dati personali
Al momento vive a...?
o
Roma
o
Napoli
o
Firenze
o
Milano
o
Torino
o
Altro
Indichi la Sua età.
.....
Lei è:
o
Uomo
o
Donna
A quanto ammonta il Suo reddito netto mensile?
o
! 0-500
o
! 1.000 – 1.500
o
! 2.000 – 2.500
o
! 500-1.000
o
! 1.500 – 2.000
o
Più di ! 2.500
Qual è la Sua professione attuale?
o
Studente
o
Lavoratore dipendente
o
In congedo per maternità
o
Lavoratore autonomo
o
Disoccupato
o
Altro (si prega di
specificare):
Qual è il livello di istruzione più elevato da Lei conseguito?
o
Scuole elementari /
medie inferiori
o
Università (< 2 anni)
o
Laurea
o
Scuole medie superiori
o
Università (2 anni)
o
Master
2. Parte A – Industria bancaria
Per iniziare, desidereremmo ricevere alcune informazioni in merito alla Sua opinione generale sull’industria dei
servizi finanziari.
!"
102 Appendices
Come valuterebbe la Sua familiarità con l’industria bancaria?
Non mi è affatto
familiare
Non mi è molto
familiare
Mi è abbastanza
familiare
Mi è molto
familiare
Come valuterebbe la capacità di innovazione delle aziende e dei servizi nell’industria bancaria, laddove “innovazione”
significa offrire prodotti adeguati alle necessità future dei consumatori?
Per niente
innovativi
O
Molto
innovativi
O
O
O
O
O
O
Come valuterebbe il design dei servizi nell’industria bancaria, laddove “design” significa aspetto e stile?
Per niente
interessante
O
Molto
interessante
O
O
O
O
O
O
Come valuterebbe il prestigio delle aziende e dei servizi nell’industria bancaria, laddove “prestigio” significa esclusività,
status, reputazione del marchio e varietà di servizi?
Per niente
prestigioso
O
Molto
prestigioso
O
O
O
O
O
O
Come valuterebbe l’affidabilità e la qualità dei servizi nell’industria bancaria?
Per niente
affidabili
O
Molto
affidabili
O
O
O
O
Con che frequenza è in contatto con la Sua banca?
Ogni 6 mesi o
meno spesso
Ogni 2-3
mesi
Ogni mese
Ogni settimana
o più spesso
!"
103 O
O
Appendices
3. Parte B – Marchi
Ora Le porremo alcune domande in merito alla Sua opinione personale su due banche: Deutsche Bank e
Commerzbank.
Conosce il Paese d’origine dei due marchi?
Se sì, indichi il Paese qui
sotto
Non so (apponga un
segno di spunta)
Deutsche Bank
Commerzbank
Ha mai avuto contatti privati o professionali con questi marchi?
No, mai
Sì, una volta
Sì, 2-3 volte
Sì, più di 3 volte
Deutsche Bank
Commerzbank
Come valuterebbe la Sua familiarità con questi marchi?
Non mi sono
affatto familiari
Non mi sono
molto familiari
Mi sono abbastanza
familiari
Mi sono molto
familiari
Deutsche Bank
Commerzbank
Come valuterebbe la capacità di innovazione dei due marchi, laddove “innovazione” significa offrire prodotti adeguati alle
necessità future dei consumatori?
Per niente
innovativo
Molto
innovativo
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
!
104 Appendices
Come valuterebbe il design dei servizi dei due marchi, laddove “design” significa aspetto e stile?
Per niente
interessante
Molto
interessante
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
Come valuterebbe il prestigio dei due marchi, laddove “prestigio” significa esclusività, status, reputazione del marchio e
varietà di servizi?
Per niente
prestigioso
Molto
prestigioso
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
Come valuterebbe l’affidabilità e la qualità dei due marchi?
Per niente
affidabile
Molto
affidabile
Deutsche Bank
O
O
O
O
O
O
O
Commerzbank
O
O
O
O
O
O
O
Se stesse cercando una banca a scopo personale, con quale probabilità sceglierebbe una di queste aziende (0 = sicuramente
no, 100 = sicuramente sì)?
Numero di punti
Deutsche Bank
...
Numero di punti
Commerzbank
...
4. Parte C – Germania
Ora Le porremo alcune domande in merito alle Sue opinioni personali sulla Germania.
!
105 Appendices
È mai stato in Germania?
No, mai
Sì, una volta
Sì, 2-3 volte
Sì, più di 3 volte
Intrattiene rapporti privati o professionali con la Germania?
Nessun
rapporto
Rapporti
saltuari
Intensi
rapporti
Come valuterebbe la Sua familiarità con la Germania?
Non mi è affatto
familiare
Non mi è molto
familiare
Mi è abbastanza
familiare
Mi è molto
familiare
Come valuterebbe la capacità di innovazione dei prodotti e dei servizi tedeschi, laddove “innovazione” significa offrire
prodotti adeguati alle necessità future dei consumatori?
Per niente
innovativi
O
Molto
innovativi
O
O
O
O
O
O
Come valuterebbe il design dei prodotti e dei servizi tedeschi, laddove “design” significa aspetto e stile?
Per niente
interessante
O
Molto
interessante
O
O
O
O
O
O
Come valuterebbe il prestigio dei prodotti e dei servizi tedeschi, laddove “prestigio” significa esclusività, status, reputazione
del marchio e varietà di servizi?
Per niente
prestigiosi
O
Molto
prestigiosi
O
O
O
O
106 O
O
Appendices
Come valuterebbe l’affidabilità e la qualità dei prodotti e dei servizi tedeschi?
Per niente
affidabili
O
Molto
affidabili
O
O
O
O
O
O
5. Parte D – Opinioni personali
Ora desideriamo porLe alcune domande in merito alle Sue opinioni personali sulle importazioni.
Gli italiani non dovrebbero acquistare prodotti stranieri, perché ciò nuoce al commercio italiano e porta disoccupazione.
Totalmente in
disaccordo
Abbastanza
in disaccordo
Né d'accordo né
in disaccordo
Abbastanza
d’accordo
Pienamente
d’accordo
Non è bene acquistare prodotti stranieri, in quanto ciò lascia gli italiani senza lavoro.
Totalmente in
disaccordo
Abbastanza
in disaccordo
Né d'accordo né
in disaccordo
Abbastanza
d’accordo
Pienamente
d’accordo
Un vero italiano dovrebbe sempre acquistare prodotti italiani.
Totalmente in
disaccordo
Abbastanza
in disaccordo
Né d'accordo né
in disaccordo
Abbastanza
d’accordo
Pienamente
d’accordo
Preferisco sempre i prodotti italiani ai prodotti stranieri.
Totalmente in
disaccordo
Abbastanza
in disaccordo
Né d'accordo né
in disaccordo
Abbastanza
d’accordo
Pienamente
d’accordo
Dovremmo acquistare prodotti fabbricati in Italia, anziché lasciare che altri Paesi si arricchiscano alle nostre spalle.
Totalmente in
disaccordo
Abbastanza
in disaccordo
Né d'accordo né
in disaccordo
Abbastanza
d’accordo
!
107 Pienamente
d’accordo
Appendices
Appendix D: Descriptive Statistics of Familiarities
Mean
Median
Standard
Deviation
Variance
Skewness
Kurtosis
Industry
Familiarity
Contact
2.57
2.85
3.00
3.00
0.71
1.02
0.50
1.04
0.11
-0.37
-0.27
-1.03
Deutsche Bank
Familiarity
Contact
1.78
1.65
2.00
1.00
0.73
0.96
0.54
0.93
0.81
1.38
0.69
0.75
Commerzbank
Familiarity
Contact
1.11
1.07
1.00
1.00
0.39
0.37
0.16
0.14
4.45
5.99
23.89
38,47
Germany
Familiarity
Contact
Relations*
2.03
1.73
1.42
2.00
1.00
1.00
0.68
0.97
0.57
0.47
0.94
0.33
0.36
1.08
1.01
0.29
-0,03
0.04
Industry
Familiarity
Contact
2.56
2.85
3.00
3.00
0.73
0.86
0.53
0.74
-0.22
-0.37
-0.18
-0.48
Deutsche Bank
Familiarity
Contact
2.15
1.54
2.00
1.00
0.87
0.93
0.76
0.86
0.22
1.69
-0.76
1.73
Commerzbank
Familiarity
Contact
1.35
1.04
1.00
1.00
0.62
0.19
0.39
0.03
1.75
5.10
2.66
24.33
Germany
Familiarity
2.16
Contact
2.16
Relations*
1.33
2.00
2.00
1.00
0.68
1.05
0.54
0.46
1.10
0.30
0.20
0.42
1.39
0.01
-1.04
1.00
SPAIN
ITALY
Appendix E: Descriptive Statistics of Behavioural Intention
Mean
Median
Standard
Deviation
Variance
Skewness
Kurtosis
Spain
Deutsche Bank
Commerzbank
42.49
20.46
50.00
10.00
28.96
23.67
838.63
560.31
0.00
1.14
-1.07
0.77
Italy
Deutsche Bank
Commerzbank
48.92
29.18
50.00
20.00
30.02
26.01
901.41
680.93
-0.19
0.64
-0.97
-0.32
108 Appendices
Appendix F: Independent Samples t-test for Intercountry Comparison of Stimuli
Levene's Test for Equality of
Variances
t-test for Equality of Means
95% Confidence
Interval of the
Difference
II
BIDB
BICB
CoI
CE
Equal variances
assumed
Equal variances not
assumed
Equal variances
assumed
Equal variances not
assumed
Equal variances
assumed
Equal variances not
assumed
Equal variances
assumed
Equal variances not
assumed
Equal variances
assumed
Equal variances not
assumed
F
10,657
,013
,168
1,169
,500
Sig.
t
,001
,911
,683
,280
,480
df
-,540
Sig. (2-tailed)
284
,590
Mean
Difference
-,07235
Std. Error
Difference
,13410
Lower
-,33631
Upper
,19161
-,539
265,390
,591
-,07235
,13434
-,33686
,19215
-,758
284
,449
-,11387
,15013
-,40939
,18164
-,759
283,900
,449
-,11387
,15010
-,40932
,18158
-3,278
284
,001
-,46445
,14167
-,74330
-,18559
-3,277
282,975
,001
-,46445
,14172
-,74340
-,18549
,903
284
,367
,12507
,13847
-,14748
,39762
,904
281,652
,367
,12507
,13836
-,14728
,39743
-2,430
284
,016
-,25554
,10514
-,46249
-,04858
-2,431
283,817
,016
-,25554
,10511
-,46244
-,04863
109 Appendices
Appendix G: Paired Samples t-test for Intracountry Comparison of BI
Spain
BIDB - BICB
Mean
,98438
Italy
BIDB - BICB
,63380
Paired Differences
95% Confidence Interval of
the Difference
Std.
Std. Error
Deviation
Mean
Lower
Upper
1,20104
,10009
,78653
1,18222
,98196
,08240
110 ,47090
,79671
t
9,835
df
143
Sig. (2-tailed)
,000
7,691
141
,000
Appendices
Appendix H: Paired Samples t-test for Intracountry Comparison of Familiarities
Mean
t
df
Sig. (2-tailed)
Spain
Industry
Familiarity – Contact
-,278
1,047
,087
-,450
-,105
-3,182
143
,002
Deutsche Bank
Contact – Familiarity
-,132
,813
,068
-,266
,002
-1,948
143
,053
Commerzbank
-,042
,261
,022
-,085
,001
-1,915
143
,058
Germany
Contact – Relations
,313
,897
,075
,165
,460
4,183
143
,000
-,306
,787
,066
-,435
-,176
-4,659
143
,000
Relations – Familiarity
-,618
,637
,053
-,723
-,513
-11,646
143
,000
Italy
Industry
Familiarity – Contact
-,282
,956
,080
-,440
-,123
-3,513
141
,001
Deutsche Bank
-,613
,882
,074
-,759
-,466
-8,277
141
,000
Commerzbank
-,317
,588
,049
-,414
-,219
-6,423
141
,000
Germany
Contact – Relations
,831
1,010
,085
,663
,999
9,801
141
,000
,000
,938
,079
-,156
,156
,000
141
1,000
-,831
,714
,060
-,949
-,712
-13,863
141
,000
Relations – Familiarity
Paired Differences
the Difference
Std.
Std. Error
Deviation
Mean
Lower
Upper
111 Appendices
Appendix I: Independent Samples t-test for Intercountry Comparison of BeI
Levene's Test for Equality of
Variances
t-test for Equality of Means
95% Confidence
Interval of the
Difference
F
BeIDB
BeICB
Equal variances
assumed
Equal variances not
assumed
Equal variances
assumed
Equal variances not
assumed
Sig.
,022
3,418
t
,881
,066
df
-1,843
Sig. (2-tailed)
284
,066
Mean
Difference
-6,42948
Std. Error
Difference
3,48792
Lower
-13,29493
Upper
,43597
-,1843
283,289
,066
-6,42948
3,48880
-13,29674
,43779
-2,962
284
,003
-8,72477
2,94524
-14,52204
-2,92749
-2,960
280,534
,003
-8,72477
2,94725
-15,52630
-2,92323
Appendix J: Paired Samples t-test for Intracountry Comparison of BeI
Spain
BIDB - BICB
Mean
,98438
Italy
BIDB - BICB
,63380
Paired Differences
the Difference
Std.
Std. Error
Deviation
Mean
Lower
Upper
1,20104
,10009
,78653
1,18222
,98196
,08240
112 ,47090
,79671
t
df
9,835
143
Sig. (2-tailed)
,000
7,691
141
,000
Appendices
Appendix K: Correlation of Stimuli and Familiarities (Spain)
* significant at p < 0.5
** significant at p < 0.01
Ind_contact
Ind_familiarity
II
DB_contact
DB_familiarity
BIDB
DB_
familiarity
,076
BIDB
,187*
BeIDB
,208*
,006
144
,370**
,366
144
,354**
,025
144
,267**
,012
144
,140
144
,313**
,000
144
1
,000
144
,114
,000
144
,191*
,001
144
,454**
,095
144
,296**
,079
144
,000
144
144
,176
144
,022
144
,000
144
,000
144
Pearson
Correlation
Sig. (2-tailed)
,229**
,370**
,114
1
,570**
,365**
,274**
,006
,000
,176
,000
,000
,001
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
144
,076
144
,354**
144
,191*
144
,570**
144
1
144
,480**
144
,470**
,366
144
,187*
,000
144
,267**
,022
144
,454**
,000
144
,365**
144
,480**
,000
144
1
,000
144
,605**
,025
144
,001
144
,000
144
,000
144
,000
144
144
,000
144
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Ind_contact
1
Ind_
familiarity
,306**
144
,306**
,000
144
1
,079
144
,313**
,000
144
,147
II
DB_contact
,147
,229**
113 Appendices
BeIDB
CB_contact
CB_familiarity
BICB
BeICB
CoO_contact
CoO_relations
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Ind_contact
,208*
Ind_
familiarity
,140
,012
144
,095
144
,000
144
*
**
,197
,224
DB_
familiarity
,470**
BIDB
,605**
,001
144
,000
144
,000
144
144
**
,149
,110
,032
-,132
II
DB_contact
,296**
,274**
,264
BeIDB
1
,018
144
,147
,007
144
,248**
,001
144
,331**
,075
144
,196*
,191
144
,231**
,706
144
,153
,115
144
-,014
,079
144
,072
,003
144
,017
,000
144
,262**
,018
144
,076
,005
144
,108
,067
144
,527**
,868
144
,123
,394
144
,114
,841
144
,097
,002
144
,244**
,365
144
,192*
,199
144
,254**
,000
144
,335**
,141
144
,558**
,172
144
,246
144
,003
144
,021
144
,002
144
,000
144
,000
144
Pearson
Correlation
Sig. (2-tailed)
,064
,268**
,141
,458**
,357**
,290**
,208*
,446
,001
,091
,000
,000
,000
,012
N
Pearson
Correlation
Sig. (2-tailed)
N
144
,146
144
,222**
144
,044
144
,408**
144
,338**
144
,207*
144
,276**
,082
144
,008
144
,603
144
,000
144
,000
144
,013
144
,001
144
114 Appendices
CoO_familiarity
CoI
CE
Ind_contact
,088
Ind_
familiarity
,162
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
,295
144
,048
,053
144
,166*
,520
144
,268**
,567
,046
N
144
Pearson
Correlation
Pearson
Correlation
Sig. (2-tailed)
N
DB_
familiarity
,364**
BIDB
,297**
BeIDB
,250**
,001
144
,307**
,000
144
,328**
,000
144
,652**
,002
144
,359**
,001
,000
,000
,000
,000
144
144
144
144
144
144
-,072
,018
,071
-,096
-,082
-,002
-,055
,394
,828
,396
,252
,327
,983
,510
144
144
144
144
144
144
144
II
DB_contact
,054
,284**
115 Appendices
Ind_contact
Ind_familiarity
II
DB_contact
DB_familiarity
BIDB
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
CB_contact
,197*
CB_
familiarity
,147
BICB
,072
BeICB
,114
CoO_
contact
,064
CoO_
relations
,146
CoO_
familiarity
,088
CoI
,048
CE
-,072
,018
,079
,394
,172
,446
,082
,295
,567
,394
144
,224**
144
,248**
144
,017
144
,097
144
,268**
144
,222**
144
,162
144
,166*
144
,018
,007
,003
,841
,246
,001
,008
,053
,046
,828
144
,264**
144
,331**
144
,262**
144
,244**
144
,141
144
,044
144
,054
144
,268**
144
,071
,001
,000
,002
,003
,091
,603
,520
,001
,396
144
144
144
144
144
144
144
144
144
,149
*
,076
*
**
**
**
**
-,096
,075
,018
,365
,021
,000
,000
,001
,000
,252
144
,110
144
,231**
144
,108
144
,254**
144
,357**
144
,338**
144
,364**
144
,328**
144
-,082
,191
,005
,199
,002
,000
,000
,000
,000
,327
144
,032
144
,153
144
,527**
144
,335**
144
,290**
144
,207*
144
,297**
144
,652**
144
-,002
,706
,067
,000
,000
,000
,013
,000
,000
,983
144
144
144
144
144
144
144
144
144
,196
,192
116 ,458
,408
,284
,307
Appendices
BeIDB
CB_contact
CB_familiarity
BICB
BeICB
CoO_contact
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
Pearson
Correlation
Sig. (2tailed)
N
CB_contact
-,132
CB_
familiarity
-,014
BICB
,123
BeICB
,558**
CoO_
contact
,208*
CoO_
relations
,276**
CoO_
familiarity
,250**
CoI
,359**
CE
-,055
,115
,868
,141
,000
,012
,001
,002
,000
,510
144
144
144
144
144
144
144
144
144
1
**
*
,032
,014
-,006
,018
,036
,050
,000
,011
,699
,869
,948
,829
,672
,554
144
1
144
,317**
144
,131
144
,189*
144
,072
144
,167*
144
,109
144
,057
,000
,118
,023
,390
,045
,192
,498
144
1
144
,329**
144
,059
144
,033
144
,104
144
,294**
144
,073
,000
,483
,691
,216
,000
,385
144
1
144
,168*
144
,299**
144
,184*
144
,169*
144
-,018
,044
,000
,027
,043
,826
144
144
144
144
144
1
**
**
**
-,257**
144
,767**
,767
,000
,211
144
,211*
144
,317**
,011
,000
144
,032
144
,131
144
,329**
,699
,118
,000
144
144
144
144
,014
*
,059
*
,869
,023
,483
,044
144
144
144
144
,189
,168
117 144
,418
,595
,278
,000
,000
,001
,002
144
144
144
144
Appendices
Pearson
Correlation
Sig. (2tailed)
N
CoO_familiarity Pearson
Correlation
Sig. (2tailed)
N
Pearson
CoI
Correlation
Sig. (2tailed)
N
CoO_relations
CE
Pearson
Correlation
Sig. (2tailed)
N
CB_contact
-,006
CB_
familiarity
,072
BICB
,033
BeICB
,299**
CoO_
CoO_relati
contact
ons
,418**
1
,948
,390
,691
,000
,000
144
,018
144
,167*
144
,104
144
,184*
144
,595**
144
,498**
,829
,045
,216
,027
,000
,000
144
,036
144
,109
144
,294**
144
,169*
144
,278**
144
,256**
144
,286**
,672
,192
,000
,043
,001
,002
,001
144
144
144
144
144
144
144
144
144
**
-,097
-,072
**
1
CoI
,256**
CE
-,097
,000
,002
,249
144
1
144
,286**
144
-,072
,001
,388
144
1
144
-,235**
,005
,050
,057
,073
-,018
,554
,498
,385
,826
,002
,249
,388
,005
144
144
144
144
144
144
144
144
118 -,257
CoO_
familiarity
,498**
-,235
144
Appendices
Appendix L: Correlation of Stimuli and Familiarities (Italy)
* significant at p < 0.5
** significant at p < 0.01
Ind_contact
Ind_familiarity
II
DB_contact
DB_familiarity
BIDB
Ind_contact
1
Ind_
familiarity
,287**
142
,287**
,001
142
1
,915
142
,138
,001
142
-,009
142
,138
,915
142
,102
142
Pearson
Correlation
Sig. (2-tailed)
,159
**
,058
,010
,006
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
142
,241**
142
,342**
142
,179*
142
,520**
,004
142
,064
,000
142
,104
,033
142
,579**
,453
142
,218
142
,000
142
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
,216
DB_
familiarity
,241**
BIDB
,064
BeIDB
,012
,058
142
,216**
,004
142
,342**
,453
142
,104
,883
142
,055
,102
142
1
,010
142
,230**
,000
142
,179*
,218
142
,579**
,512
142
,281**
142
,006
142
,033
142
,000
142
,001
142
,230**
1
,520**
,207*
,217**
,000
,013
,009
142
1
142
,332**
142
,384**
,000
142
,207*
142
,332**
,000
142
1
,000
142
,557**
,013
142
,000
142
142
,000
142
II
DB_contact
-,009
,159
119 Appendices
BeIDB
CB_contact
CB_familiarity
BICB
BeICB
CoO_contact
CoO_relations
Pearson
Correlation
Sig. (2-tailed)
N
Ind_contact
,012
Ind_
familiarity
,055
,883
142
,512
142
II
DB_contact
,281**
,217**
DB_
familiarity
,384**
BIDB
,557**
BeIDB
1
,001
142
,009
142
,000
142
,000
142
142
,034
,167
*
-,020
**
,142
,127
-,079
,684
142
,129
,046
142
,279**
,810
142
,005
,002
142
,036
,091
142
,292**
,132
142
,101
,352
142
,140
,125
142
-,029
,001
142
,032
,956
142
,515**
,674
142
-,057
,000
142
,029
,231
142
,685**
,097
142
,258**
,732
142
-,059
,705
142
-,052
,000
142
,029
,500
142
-,075
,734
142
,050
,000
142
,161
,002
142
,501**
,483
142
,541
142
,730
142
,373
142
,555
142
,055
142
,000
142
Pearson
Correlation
Sig. (2-tailed)
,122
,204*
-,048
,120
,011
,025
,017
,148
,015
,574
,153
,895
,771
,842
N
Pearson
Correlation
Sig. (2-tailed)
N
142
,171*
142
,117
142
-,013
142
,219**
142
,071
142
,053
142
,094
,041
142
,165
142
,882
142
,009
142
,401
142
,534
142
,264
142
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
120 ,260
Appendices
CoO_familiarity
CoI
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
CE
Pearson
Correlation
Sig. (2-tailed)
N
Ind_contact
,140
Ind_
familiarity
,144
DB_
familiarity
,125
BIDB
,067
BeIDB
,103
,097
142
,036
,088
142
,078
,624
142
,410**
,950
142
,036
,138
142
,073
,427
142
,486**
,221
142
,289**
,668
,354
,000
,674
,390
,000
,000
142
142
142
142
142
142
142
-,029
-,041
*
,139
,129
,041
,069
,735
,627
,030
,100
,125
,632
,412
142
142
142
142
142
142
142
II
DB_contact
,042
-,005
,183
121 Appendices
Ind_contact
Ind_familiarity
II
DB_contact
DB_familiarity
BIDB
CB_contact
,034
CB_
familiarity
,129
BICB
-,029
,684
142
,167*
,125
142
,279**
,732
142
,032
,483
142
-,052
,046
142
-,020
,001
142
,005
,705
142
,515**
,810
142
,956
142
Pearson
Correlation
Sig. (2-tailed)
,260**
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
CoO_
relations
,171*
CoO_
familiarity
,140
,148
142
,204*
,041
142
,117
,541
142
,029
,015
142
-,048
,000
142
,730
142
,036
-,057
,002
,674
142
,142
BeICB
CoO_contact
-,059
,122
,036
CE
-,029
,097
142
,144
,668
142
,078
,735
142
-,041
,165
142
-,013
,088
142
,042
,354
142
,410**
,627
142
,183*
,574
142
,882
142
,624
142
,000
142
,030
142
-,075
,120
,219**
-,005
,036
,139
,500
,373
,153
,009
,950
,674
,100
142
,292**
142
,029
142
,050
142
,011
142
,071
142
,125
142
,073
142
,129
,091
142
,127
,000
142
,101
,734
142
,685**
,555
142
,161
,895
142
,025
,401
142
,053
,138
142
,067
,390
142
,486**
,125
142
,041
,132
142
,231
142
,000
142
,055
142
,771
142
,534
142
,427
142
,000
142
,632
142
122 CoI
Appendices
BeIDB
CB_contact
CB_familiarity
BICB
BeICB
CoO_contact
CoO_relations
CoO_
relations
,094
CoO_
familiarity
,103
CoI
,289**
,842
142
,264
142
,221
142
,000
142
,412
142
,087
,117
,166*
,123
,001
,049
,031
142
,205*
,304
142
,211*
,167
142
,282**
,049
142
,115
,143
142
,401**
,991
142
,070
,565
142
-,172*
142
,205*
,014
142
1
,012
142
,347**
,001
142
-,013
,174
142
-,010
,000
142
,061
,409
142
,359**
,041
142
,082
,031
142
,087
,014
142
,211*
142
,347**
,000
142
1
,877
142
-,027
,906
142
,004
,474
142
,127
,000
142
,042
,334
142
,075
,304
142
,012
142
,000
142
142
,752
142
,960
142
,131
142
,617
142
,373
142
Pearson
Correlation
Sig. (2-tailed)
,117
,282**
-,013
-,027
1
,329**
,480**
,086
-,053
,167
,001
,877
,752
,000
,000
,308
,529
N
Pearson
Correlation
Sig. (2-tailed)
N
142
,166*
142
,115
142
-,010
142
,004
142
,329**
142
1
142
,334**
142
-,006
142
,048
,049
142
,174
142
,906
142
,960
142
,000
142
142
,000
142
,944
142
,574
142
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
CB_contact
-,079
CB_
familiarity
,140
BICB
,258**
,352
142
,097
142
,002
142
,000
142
1
,323**
,181*
142
,323**
,000
142
1
,000
142
,181*
BeICB
CoO_contact
**
,501
,017
123 CE
,069
Appendices
CoO_familiarity
CoI
CB_contact
,123
CB_
familiarity
,401**
,143
142
,001
,000
142
,070
,474
142
,359**
,131
142
,042
,000
142
,086
,991
,409
,000
,617
142
142
142
Pearson
Correlation
Sig. (2-tailed)
,049
*
,565
N
142
Pearson
Correlation
Sig. (2-tailed)
N
Pearson
Correlation
Sig. (2-tailed)
N
CE
CoO_
familiarity
CoI
,229**
,000
142
-,006
142
,229**
,006
142
1
,308
,944
,006
142
142
142
142
142
142
,082
,075
-,053
,048
,009
-,002
1
,041
,334
,373
,529
,574
,918
,981
142
142
142
142
142
142
142
,061
BeICB
CoO_contact
,127
,480**
CoO_
relations
,334**
1
-,172
BICB
124 CE
,009
,918
142
-,002
,981
142
Appendices
Appendix M: Multiple Regression on BI (DB, Spain)
Change Statistics
Model
1
R
,809
R Square
,654
Adjusted R
Square
,631
Std. Error of
the Estimate
,78379
R Square
Change
,654
F Change
28,150
df1
df2
9
134
Sig. F
Change
,000
Durbin-Watson
1,913
Dependent variable: BIDB
Model
1
(Constant)
95,0% Confidence Interval
for B
t
-4,119
Sig.
Lower Bound Upper Bound
,000
-3,417
-1,200
Correlations
Zero-order
Partial
Collinearity Statistics
Part
Tolerance
VIF
CE
,218
,076
,153
2,863
,005
,067
,368
-,002
,240
,145
,905
1,105
CoI
,584
,061
,557
9,606
,000
,464
,705
,652
,639
,488
,767
1,304
II
,313
,072
,238
4,347
,000
,170
,455
,454
,352
,221
,861
1,162
Ind_contact
,149
,069
,118
2,161
,032
,013
,286
,187
,184
,110
,865
1,156
DB_familiarity
,469
,097
,267
4,852
,000
,278
,661
,480
,387
,247
,854
1,171
SD_age
SD_income
SD_profession_
student
SD_profession_
unemployed
Unstandardized Standardized
Coefficients
Coefficients
Std.
B
Error
Beta
-2,308
,560
,028
,010
,162
2,762
,007
,008
,048
,077
,232
,140
,748
1,336
-,141
,055
-,153
-2,571
,011
-,250
-,033
,095
-,217
-,131
,726
1,377
,547
,263
,122
2,081
,039
,027
1,067
,090
,177
,106
,752
1,329
-,554
,247
-,119
-2,238
,027
-1,043
-,064
-,201
-,190
-,114
,913
1,095
125 Appendices
Appendix N: Multiple Regression on BI (CB, Spain)
Change Statistics
Model
1
R
,451
R Square
,203
Adjusted R
Square
,186
Std. Error of
the Estimate
1,05552
R Square
Change
,203
F Change
11,911
df1
df2
3
140
Sig. F
Change
,000
Durbin-Watson
1,995
Dependent variable: BICB
Model
1
(Constant)
Coefficients
Coefficients
Std.
Error
B
Beta
,398
,450
for B
t
,885
Sig.
,378
-,491
1,287
Correlations
Zero-order
Partial
Part
Tolerance
VIF
CoI
,275
,073
,290
3,779
,000
,131
,420
,294
,304
,285
,968
1,033
CB_familiarity
,875
,225
,295
3,882
,000
,429
1,321
,317
,312
,293
,987
1,013
SD_gender
,447
,181
,189
2,472
,015
,089
,805
,131
,204
,186
,977
1,023
126 Appendices
Appendix O: Multiple Regression on BI (DB, Italy)
Change Statistics
Model
1
R
,681
R Square
,464
Adjusted R
Square
,452
Std. Error of
the Estimate
,92396
R Square
Change
,464
F Change
39,792
df1
df2
3
138
Sig. F
Change
,000
Durbin-Watson
1,858
Dependent variable: BIDB
Model
1
(Constant)
Coefficients
Coefficients
Std.
Error
B
Beta
,182
,390
for B
t
,467
Sig.
,641
-,589
,954
Correlations
Zero-order
Partial
Part
Tolerance
VIF
II
,408
,068
,415
5,987
,000
,273
,543
,579
,454
,373
,809
1,235
CoI
,336
,077
,298
4,365
,000
,184
,489
,486
,348
,272
,832
1,202
DB_familiarity
,339
,091
,236
3,731
,000
,160
,519
,332
,303
,233
,968
1,033
127 Appendices
Appendix P: Multiple Regression on BI (CB, Italy)
Change Statistics
Model
1
R
,661
R Square
,437
Adjusted R
Square
,398
Std. Error of
the Estimate
,95050
R Square
Change
,437
F Change
11,375
df1
df2
9
132
Sig. F
Change
,000
Durbin-Watson
2,072
Dependent variable: BICB
Model
1
(Constant)
for B
t
-,503
Sig.
,616
-1,603
,953
Correlations
Zero-order
Partial
Part
Tolerance
VIF
II
,422
,071
,437
5,927
,000
,281
,563
,515
,458
,387
,784
1,275
CoI
,223
,080
,202
2,782
,006
,064
,382
,359
,235
,182
,813
1,231
SD_gender
,470
,177
,191
2,657
,009
,120
,820
,120
,225
,174
,826
1,211
SD_profession
_student
SD_profession
_other
SD_profession
_unemployed
BS_familiarity
-,676
,319
-,142 -2,120
,036
-1,307
-,045
-,105
-,181
-,138
,956
1,046
-,915
,361
-,173 -2,534
,012
-1,629
-,201
-,107
-,215
-,166
,918
1,089
-,873
,330
-,183 -2,643
,009
-1,526
-,220
-,094
-,224
-,173
,891
1,122
-,277
,121
-,164 -2,286
,024
-,516
-,037
,032
-,195
-,149
,824
1,214
CB_contact
1,131
,464
,171
2,435
,016
,212
2,049
,181
,207
,159
,869
1,151
,387
,144
,196
2,692
,008
,102
,671
,205
,228
,176
,806
1,241
CB_familiarity
Coefficients
Coefficients
Std.
Error
B
Beta
-,325
,646
128 Appendices
Appendix Q: Multiple Regression on BeI (DB, Spain)
Change Statistics
Model
1
R
,638
a
R Square
,407
Adjusted R
Square
,399
Std. Error of
the Estimate
22,44896
R Square
Change
,407
F Change
48,483
df1
df2
2
141
Sig. F
Change
,000
Durbin-Watson
1,991
Dependent variable: BeIDB
Model
1
(Constant)
BIDB
DB_familiarity
Coefficients
Coefficients
Std.
Error
B
Beta
-17,679 6,391
for B
t
-2,766
Sig.
,006
-30,314
-5,045
Correlations
Zero-order
Partial
Part
Tolerance
VIF
11,050
1,659
,492
6,660
,000
7,770
14,331
,605
,489
,432
,769
1,300
9,232
2,919
,234
3,163
,002
3,461
15,002
,470
,257
,205
,769
1,300
129 Appendices
Appendix R: Multiple Regression on BeI (CB, Spain)
Change Statistics
Model
1
R
,437
a
R Square
,191
Adjusted R
Square
,180
Std. Error of
the Estimate
21,43965
R Square
Change
,191
F Change
16,656
df1
df2
2
141
Sig. F
Change
,000
Durbin-Watson
1,983
Dependent variable: BeICB
Model
1
(Constant)
BICB
CoO_relations
Coefficients
Coefficients
Std.
Error
B
Beta
-15,613 6,504
for B
t
-2,401
Sig.
,018
-28,471
-2,755
Correlations
Zero-order
Partial
Part
Tolerance
VIF
6,462
1,533
,319
4,215
,000
3,431
9,493
,329
,335
,319
,999
1,001
11,889
3,129
,288
3,799
,000
5,703
18,075
,299
,305
,288
,999
1,001
130 Appendices
Appendix S: Multiple Regression on BeI (DB, Italy)
Change Statistics
Model
1
R
,611
a
R Square
,374
Adjusted R
Square
,360
Std. Error of
the Estimate
24,01576
R Square
Change
,374
F Change
27,456
df1
df2
3
138
Sig. F
Change
,000
Durbin-Watson
1,994
Dependent variable: BeIDB
Model
1
(Constant)
BIDB
Coefficients
Coefficients
Std.
Error
B
Beta
-5,683 8,801
for B
t
-,646
Sig.
,519
-23,085
11,718
Correlations
Zero-order
Partial
Part
Tolerance
VIF
11,171
1,731
,464
6,452
,000
7,748
14,595
,557
,481
,435
,876
1,142
DB_familiarity
8,471
2,494
,245
3,397
,001
3,540
13,401
,384
,278
,229
,870
1,149
SD_education
-2,785
1,363
-,140 -2,042
,043
-5,481
-,089
-,149
-,171
-,138
,972
1,029
131 Appendices
Appendix T: Multiple Regression on BeI (CB, Italy)
Change Statistics
Model
1
R
,398
a
R Square
,158
Adjusted R
Square
,146
Std. Error of
the Estimate
24,11327
R Square
Change
,158
F Change
13,062
df1
df2
2
139
Sig. F
Change
,000
Durbin-Watson
1,850
Dependent variable: BeICB
Model
1
(Constant)
BICB
SD_education
Coefficients
Coefficients
Std.
Error
B
Beta
17,689 8,205
6,629
1,684
-3,439
1,372
,311
for B
t
2,156
Sig.
,033
1,467
33,911
Correlations
Zero-order
Partial
Part
Tolerance
VIF
3,936
,000
3,299
9,958
,347
,317
,306
,968
1,033
-,198 -2,507
,013
-6,151
-,727
-,254
-,208
-,195
,968
1,033
132 Appendices
Appendix U: Multiple Regression on CoI (Spain)
Change Statistics
Model
1
R
,451
a
R Square
,203
Adjusted R
Square
,186
Std. Error of
the Estimate
1,10990
R Square
Change
,203
F Change
11,915
df1
df2
3
140
Sig. F
Change
,000
Durbin-Watson
1,887
Dependent variable: CoI
Model
1
(Constant)
CE
Coefficients
Coefficients
Std.
Error
B
Beta
3,589
,488
-,283
,104
II
,313
,096
,250
SD_education
,210
,061
,265
for B
t
7,355
-,208 -2,710
Sig.
,000
2,624
4,553
Correlations
Zero-order
Partial
Part
Tolerance
VIF
,008
-,489
-,076
-,235
-,223
-,204
,963
1,039
3,267
,001
,123
,502
,268
,266
,246
,976
1,025
3,424
,001
,089
,331
,331
,278
,258
,953
1,050
133 Appendices
Appendix V: Multiple Regression on CoI (Italy)
Change Statistics
Model
1
R
,462
a
R Square
,213
Adjusted R
Square
,202
Std. Error of
the Estimate
,98894
R Square
Change
,213
F Change
18,847
df1
df2
2
139
Sig. F
Change
,000
Durbin-Watson
1,689
Dependent variable: CoI
Model
1
(Constant)
II
CoO_familiarity
Unstandardized
Standardized
Coefficients
Coefficients
Std.
Error
B
Beta
2,655
,367
for B
t
7,228
Sig.
,000
1,928
3,381
Correlations
Zero-order
Partial
Part
Tolerance
VIF
,350
,066
,401
5,328
,000
,220
,480
,410
,412
,401
,998
1,002
,346
,123
,213
2,826
,005
,104
,588
,229
,233
,213
,998
1,002
134 Appendices
Appendix W: Abstract (German)
In der jüngeren Vergangenheit haben Forscher vermehrt die Existenz des so genannten
Herkunftslandeffektes bezweifelt. Dieser besagt, dass das Ursprungsland (UL) eines
Produktes, einer Marke oder Dienstleistung Einfluss auf deren Evaluation
beziehungsweise die Intention diese zu konsumieren hat. Die vorliegende Arbeit hat mit
Hilfe einer internationalen Studie untersucht, inwiefern das Image des UL einer Marke
die Evaluation derselben bzw. die Intention, mit dieser in Kontakt zu treten, beeinflusst.
Dabei wird eindeutig gezeigt: Je stärker einem Konsumenten bewusst ist (gemacht
wird), aus welchem Land eine Marke (angeblich) stammt, desto stärker projiziert er das
Image dieses Landes auf jenes der Marke. Diese Studie ist somit ein Beleg dafür, dass
das UL eines Produktes, einer Marke oder Dienstleistungen von hoher Bedeutung ist.
Darüber hinaus wurde der Einfluss diverser Variablen auf die Evaluation eines Landes
sowie einer ausländischen Marke, sowie die Intention, letztere zu konsumieren,
überprüft. Die Ergebnisse zeigen, dass ein höherer sogenannter Ethnozentrismus weder
zwangsläufig dazu führt, dass eine der beiden schlechter beurteilt wird, noch dadurch
die Intention, mit letzterer in Kontakt zu treten, sinkt. Darüber hinaus wird keiner dieser
drei in einem messbaren Rahmen von soziodemografischen Charakteristika von
Konsumenten beeinflusst. Ein weiteres interessantes Resultat ist die Tatsache, dass eine
höhere Vertrautheit mit dem Herkunftsland einer Marke, oder ihrer Branche, nicht
zwangsläufig zu einer besseren Evaluation derselben führt. Insgesamt bietet diese
Arbeit wichtige Einblicke in die Funktionsweise des Images eines UL und assoziierten
Konstrukten. Diese Ergebnisse sind sowohl für Akademiker, als auch für Praktiker von
hoher Relevanz. Erstere erhalten einen tiefgehenderen Einblick in die Funktionsweise
des Herkunftslandeffektes und gleichzeitig einen Beweis für seine wirtschaftliche
Relevanz. Zweitere wiederum können die Ergebnisse dieser Studie in der Er- und
Überarbeitung der Kommunikationsstrategie ihrer Marke(n) nutzen.
135 Appendices
Appendix X: Curriculum Vitae
CURRICULUM VITAE
PERSONAL DATA
Name
Date of birth
Citizenship
Paul René Frigo
Nov 12th 1983
Austrian
FORMATION
Sept 06 – Jun 07
Since 2004
1997-2002
Exchange Student at Université Dauphine de Paris (ERASMUS)
International Business Administration at University of Vienna
Commercial High School with main focus on languages (Vienna)
FURTHER EDUCATION
2002 – 2009
2003
2005
2008
Diverse Seminars (e.g.: elocution, negotiation, event management)
Certified ambulance officer
Diverse journalism seminars
Instructor for Journalists (Friedrich Funder Institute)
WORK EXPERIENCE
Since Sept 10
Since Apr 10
Since Sept 08
Jan 09 – Apr 10
Jul 07 – Jan 09
Jul 06 – Sept 06
Jun 06
Oct 05 – May 08
Jul 05
Feb 03 – Jan 04
Nov 02 – Jun 06
Jul 02 – Jul 05
Deutsche Bank – Brand Management (Frankfurt); project manager (freelancer)
urban communications – communications agency; CEO
Friedrich Funder Institute – seminar-instructor („the power of the media“)
pr frigo – PR agency; CEO
Deutsche Bank –Brand Management (Frankfurt); project manager (freelancer)
Deutsche Bank – pr-division (Frankfurt); project assistant (intern)
Austrian community Alliance – lobbying; Online-journalist (intern)
Hörsaal 10 (University newspaper); founder, editor in chief, layout, marketing
KURIER (Austrian newspaper); Journalist, (intern)
Austrian Red Cross; rescue service; ambulance officer (civilian servant)
Securitas; Event security (sport and music events)
property management FRIGO; computer technician, field service, (employee)
OUTSIDE ACTIVITIES:
02/06 – 06/08
Since 02/04
OeH (students lobby); counsellor, layout, project manager
Austrian Red Cross; rescue service; ambulance officer (volunteer)
LANGUAGES
German
English
French
Spanish
Mother Tongue
Very high knowledge
advanced knowledge (CFS (Certificat de français du secrétariat (2002)))
basic knowledge
136 Appendices
the rest is silence
(Hamlet)
137

diplomarbeit - E

Transcription

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